74
Doctorate Dissertation, L’Aquila, March 31 st 2009 1 Managing Security Issues in Advanced Applications of Wireless Sensor Networks PhD Candidate: Ing. Marco Pugliese Advisor: Prof. Fortunato Santucci PhD School Coordinator: Prof. Giuseppe Ferri Università degli Studi dell'Aquila Dipartimento di Ingegneria Elettrica e dell’Informazione Corso di Dottorato di Ricerca Ingegneria Elettrica e dell’Informazione XXI Ciclo A.A. 2007-08 SSD: ING/INF 03 Telecommunications

Managing Security Issues in Advanced Applications of Wireless Sensor Networks

  • Upload
    cargan

  • View
    42

  • Download
    0

Embed Size (px)

DESCRIPTION

Corso di Dottorato di Ricerca Ingegneria Elettrica e dell’Informazione XXI Ciclo A.A. 2007-08 SSD: ING/INF 03 Telecommunications. Managing Security Issues in Advanced Applications of Wireless Sensor Networks. PhD Candidate: Ing. Marco Pugliese Advisor: Prof. Fortunato Santucci - PowerPoint PPT Presentation

Citation preview

Page 1: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

1

Managing Security Issues in Advanced Applications of Wireless Sensor Networks

PhD Candidate Ing Marco Pugliese Advisor Prof Fortunato Santucci

PhD School Coordinator Prof Giuseppe Ferri

Universitagrave degli Studi dellAquilaDipartimento di Ingegneria Elettrica e dellrsquoInformazione

Corso di Dottorato di RicercaIngegneria Elettrica e dellrsquoInformazione

XXI CicloAA 2007-08

SSD INGINF 03 Telecommunications

Doctorate Dissertation LrsquoAquila March 31st 2009

2

bull Data Samplingbull Command Disseminationbull Data Collection

ChallengesExample of WSN-based Health

Monitoring System

Node (Mote + Accelerometer Board)

Battery

Bi-directionalPath Antenna

Node (Mote + Accelerometer Board)

Battery

Bi-directionalPath Antenna

[source Culler D et al ldquoHealth Monitoring of Civil Infrastructures Using Wireless Sensor Networksrdquo SensorNet Architecture meeting Nov 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

3

Link Layer Cryptography

Intrusion Detection System

cross-layer

Secure

Platform

Securing the Monitoring System

Base Station (sink) External server

BS

Monitoring domains

Doctorate Dissertation LrsquoAquila March 31st 2009

4

Objective amp MethodologyO Design and implementation of a comprehensive cross-layer framework to provide WSN-based monitoring services with security (data confidentiality data entity authentication) and reliability (data integrity service availability)

Pilot project WINSOME (WIreless sensor Network-based Secure system fOr structural integrity Monitoring and alErting) developed at DEWS premises

M RampD approachndash Cross-layer domain (link layer + net layer + appl layer) ndash Integration of the ldquotraditionalrdquo security techniques with novel

components and Cost Rebalancing (computation time and memory usage) to comply with WSN resource constraints

ndash Design Optimization (platform-based system design PBD)

ndash Modular SW Development (component-based sw design) ndash Dynamic Distributed Application Architecture (mobile agent-

based)

Doctorate Dissertation LrsquoAquila March 31st 2009

5

Outlinebull WINSOME PBD (I)bull Underlying Physical WSN Deploymentbull Underlying Logical WSN Deployment (ARCHEA)bull Link Layer Cryptographic Scheme (TAKS)bull WPM-based IDSbull WINSOME PBD (II)bull Next steps (near-term)bull Next steps (mid-term)

Doctorate Dissertation LrsquoAquila March 31st 2009

6

Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Application Execution Environment (AEE)

Application A1

Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Application A2 Application An

Localmemory

MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

7

Agent-based Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Mobile Agent Application Execution Environment (MA-AEE)

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

MA-MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

8

WINSOME PBD (I)

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

9

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

WINSOME PBD (I)

AGILLA-basedMA-AEE

ARCHEA(Available Resource Cluster Head Election Algorithm)

TAKS(Topology Authenticated symmetric Key Scheme)

WPM-based IDS (Weak Process Model based Intrusion Detection System)

Doctorate Dissertation LrsquoAquila March 31st 2009

10

Underlying WSNPhysical WSN Deployment

Q Given a set of Sensor Nodes find the class of WSN physical deployments (geometrical nodes distributions) compliant to coverage (redundancy vs reliability) and resource requirements

Coverage-Cost Quality IndicatorsConditions for lossless lossy

detection

Min Redundancy Configuration Fundamental

cell

3r

r

Fundamentalcell

r

Max Reliability Configuration

A

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 2: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

2

bull Data Samplingbull Command Disseminationbull Data Collection

ChallengesExample of WSN-based Health

Monitoring System

Node (Mote + Accelerometer Board)

Battery

Bi-directionalPath Antenna

Node (Mote + Accelerometer Board)

Battery

Bi-directionalPath Antenna

[source Culler D et al ldquoHealth Monitoring of Civil Infrastructures Using Wireless Sensor Networksrdquo SensorNet Architecture meeting Nov 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

3

Link Layer Cryptography

Intrusion Detection System

cross-layer

Secure

Platform

Securing the Monitoring System

Base Station (sink) External server

BS

Monitoring domains

Doctorate Dissertation LrsquoAquila March 31st 2009

4

Objective amp MethodologyO Design and implementation of a comprehensive cross-layer framework to provide WSN-based monitoring services with security (data confidentiality data entity authentication) and reliability (data integrity service availability)

Pilot project WINSOME (WIreless sensor Network-based Secure system fOr structural integrity Monitoring and alErting) developed at DEWS premises

M RampD approachndash Cross-layer domain (link layer + net layer + appl layer) ndash Integration of the ldquotraditionalrdquo security techniques with novel

components and Cost Rebalancing (computation time and memory usage) to comply with WSN resource constraints

ndash Design Optimization (platform-based system design PBD)

ndash Modular SW Development (component-based sw design) ndash Dynamic Distributed Application Architecture (mobile agent-

based)

Doctorate Dissertation LrsquoAquila March 31st 2009

5

Outlinebull WINSOME PBD (I)bull Underlying Physical WSN Deploymentbull Underlying Logical WSN Deployment (ARCHEA)bull Link Layer Cryptographic Scheme (TAKS)bull WPM-based IDSbull WINSOME PBD (II)bull Next steps (near-term)bull Next steps (mid-term)

Doctorate Dissertation LrsquoAquila March 31st 2009

6

Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Application Execution Environment (AEE)

Application A1

Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Application A2 Application An

Localmemory

MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

7

Agent-based Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Mobile Agent Application Execution Environment (MA-AEE)

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

MA-MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

8

WINSOME PBD (I)

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

9

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

WINSOME PBD (I)

AGILLA-basedMA-AEE

ARCHEA(Available Resource Cluster Head Election Algorithm)

TAKS(Topology Authenticated symmetric Key Scheme)

WPM-based IDS (Weak Process Model based Intrusion Detection System)

Doctorate Dissertation LrsquoAquila March 31st 2009

10

Underlying WSNPhysical WSN Deployment

Q Given a set of Sensor Nodes find the class of WSN physical deployments (geometrical nodes distributions) compliant to coverage (redundancy vs reliability) and resource requirements

Coverage-Cost Quality IndicatorsConditions for lossless lossy

detection

Min Redundancy Configuration Fundamental

cell

3r

r

Fundamentalcell

r

Max Reliability Configuration

A

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 3: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

3

Link Layer Cryptography

Intrusion Detection System

cross-layer

Secure

Platform

Securing the Monitoring System

Base Station (sink) External server

BS

Monitoring domains

Doctorate Dissertation LrsquoAquila March 31st 2009

4

Objective amp MethodologyO Design and implementation of a comprehensive cross-layer framework to provide WSN-based monitoring services with security (data confidentiality data entity authentication) and reliability (data integrity service availability)

Pilot project WINSOME (WIreless sensor Network-based Secure system fOr structural integrity Monitoring and alErting) developed at DEWS premises

M RampD approachndash Cross-layer domain (link layer + net layer + appl layer) ndash Integration of the ldquotraditionalrdquo security techniques with novel

components and Cost Rebalancing (computation time and memory usage) to comply with WSN resource constraints

ndash Design Optimization (platform-based system design PBD)

ndash Modular SW Development (component-based sw design) ndash Dynamic Distributed Application Architecture (mobile agent-

based)

Doctorate Dissertation LrsquoAquila March 31st 2009

5

Outlinebull WINSOME PBD (I)bull Underlying Physical WSN Deploymentbull Underlying Logical WSN Deployment (ARCHEA)bull Link Layer Cryptographic Scheme (TAKS)bull WPM-based IDSbull WINSOME PBD (II)bull Next steps (near-term)bull Next steps (mid-term)

Doctorate Dissertation LrsquoAquila March 31st 2009

6

Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Application Execution Environment (AEE)

Application A1

Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Application A2 Application An

Localmemory

MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

7

Agent-based Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Mobile Agent Application Execution Environment (MA-AEE)

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

MA-MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

8

WINSOME PBD (I)

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

9

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

WINSOME PBD (I)

AGILLA-basedMA-AEE

ARCHEA(Available Resource Cluster Head Election Algorithm)

TAKS(Topology Authenticated symmetric Key Scheme)

WPM-based IDS (Weak Process Model based Intrusion Detection System)

Doctorate Dissertation LrsquoAquila March 31st 2009

10

Underlying WSNPhysical WSN Deployment

Q Given a set of Sensor Nodes find the class of WSN physical deployments (geometrical nodes distributions) compliant to coverage (redundancy vs reliability) and resource requirements

Coverage-Cost Quality IndicatorsConditions for lossless lossy

detection

Min Redundancy Configuration Fundamental

cell

3r

r

Fundamentalcell

r

Max Reliability Configuration

A

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 4: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

4

Objective amp MethodologyO Design and implementation of a comprehensive cross-layer framework to provide WSN-based monitoring services with security (data confidentiality data entity authentication) and reliability (data integrity service availability)

Pilot project WINSOME (WIreless sensor Network-based Secure system fOr structural integrity Monitoring and alErting) developed at DEWS premises

M RampD approachndash Cross-layer domain (link layer + net layer + appl layer) ndash Integration of the ldquotraditionalrdquo security techniques with novel

components and Cost Rebalancing (computation time and memory usage) to comply with WSN resource constraints

ndash Design Optimization (platform-based system design PBD)

ndash Modular SW Development (component-based sw design) ndash Dynamic Distributed Application Architecture (mobile agent-

based)

Doctorate Dissertation LrsquoAquila March 31st 2009

5

Outlinebull WINSOME PBD (I)bull Underlying Physical WSN Deploymentbull Underlying Logical WSN Deployment (ARCHEA)bull Link Layer Cryptographic Scheme (TAKS)bull WPM-based IDSbull WINSOME PBD (II)bull Next steps (near-term)bull Next steps (mid-term)

Doctorate Dissertation LrsquoAquila March 31st 2009

6

Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Application Execution Environment (AEE)

Application A1

Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Application A2 Application An

Localmemory

MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

7

Agent-based Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Mobile Agent Application Execution Environment (MA-AEE)

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

MA-MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

8

WINSOME PBD (I)

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

9

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

WINSOME PBD (I)

AGILLA-basedMA-AEE

ARCHEA(Available Resource Cluster Head Election Algorithm)

TAKS(Topology Authenticated symmetric Key Scheme)

WPM-based IDS (Weak Process Model based Intrusion Detection System)

Doctorate Dissertation LrsquoAquila March 31st 2009

10

Underlying WSNPhysical WSN Deployment

Q Given a set of Sensor Nodes find the class of WSN physical deployments (geometrical nodes distributions) compliant to coverage (redundancy vs reliability) and resource requirements

Coverage-Cost Quality IndicatorsConditions for lossless lossy

detection

Min Redundancy Configuration Fundamental

cell

3r

r

Fundamentalcell

r

Max Reliability Configuration

A

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 5: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

5

Outlinebull WINSOME PBD (I)bull Underlying Physical WSN Deploymentbull Underlying Logical WSN Deployment (ARCHEA)bull Link Layer Cryptographic Scheme (TAKS)bull WPM-based IDSbull WINSOME PBD (II)bull Next steps (near-term)bull Next steps (mid-term)

Doctorate Dissertation LrsquoAquila March 31st 2009

6

Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Application Execution Environment (AEE)

Application A1

Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Application A2 Application An

Localmemory

MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

7

Agent-based Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Mobile Agent Application Execution Environment (MA-AEE)

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

MA-MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

8

WINSOME PBD (I)

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

9

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

WINSOME PBD (I)

AGILLA-basedMA-AEE

ARCHEA(Available Resource Cluster Head Election Algorithm)

TAKS(Topology Authenticated symmetric Key Scheme)

WPM-based IDS (Weak Process Model based Intrusion Detection System)

Doctorate Dissertation LrsquoAquila March 31st 2009

10

Underlying WSNPhysical WSN Deployment

Q Given a set of Sensor Nodes find the class of WSN physical deployments (geometrical nodes distributions) compliant to coverage (redundancy vs reliability) and resource requirements

Coverage-Cost Quality IndicatorsConditions for lossless lossy

detection

Min Redundancy Configuration Fundamental

cell

3r

r

Fundamentalcell

r

Max Reliability Configuration

A

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 6: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

6

Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Application Execution Environment (AEE)

Application A1

Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Application A2 Application An

Localmemory

MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

7

Agent-based Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Mobile Agent Application Execution Environment (MA-AEE)

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

MA-MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

8

WINSOME PBD (I)

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

9

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

WINSOME PBD (I)

AGILLA-basedMA-AEE

ARCHEA(Available Resource Cluster Head Election Algorithm)

TAKS(Topology Authenticated symmetric Key Scheme)

WPM-based IDS (Weak Process Model based Intrusion Detection System)

Doctorate Dissertation LrsquoAquila March 31st 2009

10

Underlying WSNPhysical WSN Deployment

Q Given a set of Sensor Nodes find the class of WSN physical deployments (geometrical nodes distributions) compliant to coverage (redundancy vs reliability) and resource requirements

Coverage-Cost Quality IndicatorsConditions for lossless lossy

detection

Min Redundancy Configuration Fundamental

cell

3r

r

Fundamentalcell

r

Max Reliability Configuration

A

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 7: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

7

Agent-based Distributed Architecture Platform-Based Model

Underlying WSN Deployment

Secure Platform

Mobile Agent Application Execution Environment (MA-AEE)

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

MA-MWservices

Sharedmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

8

WINSOME PBD (I)

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

9

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

WINSOME PBD (I)

AGILLA-basedMA-AEE

ARCHEA(Available Resource Cluster Head Election Algorithm)

TAKS(Topology Authenticated symmetric Key Scheme)

WPM-based IDS (Weak Process Model based Intrusion Detection System)

Doctorate Dissertation LrsquoAquila March 31st 2009

10

Underlying WSNPhysical WSN Deployment

Q Given a set of Sensor Nodes find the class of WSN physical deployments (geometrical nodes distributions) compliant to coverage (redundancy vs reliability) and resource requirements

Coverage-Cost Quality IndicatorsConditions for lossless lossy

detection

Min Redundancy Configuration Fundamental

cell

3r

r

Fundamentalcell

r

Max Reliability Configuration

A

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 8: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

8

WINSOME PBD (I)

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Doctorate Dissertation LrsquoAquila March 31st 2009

9

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

WINSOME PBD (I)

AGILLA-basedMA-AEE

ARCHEA(Available Resource Cluster Head Election Algorithm)

TAKS(Topology Authenticated symmetric Key Scheme)

WPM-based IDS (Weak Process Model based Intrusion Detection System)

Doctorate Dissertation LrsquoAquila March 31st 2009

10

Underlying WSNPhysical WSN Deployment

Q Given a set of Sensor Nodes find the class of WSN physical deployments (geometrical nodes distributions) compliant to coverage (redundancy vs reliability) and resource requirements

Coverage-Cost Quality IndicatorsConditions for lossless lossy

detection

Min Redundancy Configuration Fundamental

cell

3r

r

Fundamentalcell

r

Max Reliability Configuration

A

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 9: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

9

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

Underlying WSN Deployment

Mobile Agent Application Execution Environment (MA-AEE)

IDSAgent comp

Monitoring Applications

IDSCore comp

Link layerCryptography

WSN TopologyManager

Secure Platform

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

IntegrityMonitoring

Agent

otheragents

MA-MWservices

Sharedmemory

Localmemory

WINSOME PBD (I)

AGILLA-basedMA-AEE

ARCHEA(Available Resource Cluster Head Election Algorithm)

TAKS(Topology Authenticated symmetric Key Scheme)

WPM-based IDS (Weak Process Model based Intrusion Detection System)

Doctorate Dissertation LrsquoAquila March 31st 2009

10

Underlying WSNPhysical WSN Deployment

Q Given a set of Sensor Nodes find the class of WSN physical deployments (geometrical nodes distributions) compliant to coverage (redundancy vs reliability) and resource requirements

Coverage-Cost Quality IndicatorsConditions for lossless lossy

detection

Min Redundancy Configuration Fundamental

cell

3r

r

Fundamentalcell

r

Max Reliability Configuration

A

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 10: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

10

Underlying WSNPhysical WSN Deployment

Q Given a set of Sensor Nodes find the class of WSN physical deployments (geometrical nodes distributions) compliant to coverage (redundancy vs reliability) and resource requirements

Coverage-Cost Quality IndicatorsConditions for lossless lossy

detection

Min Redundancy Configuration Fundamental

cell

3r

r

Fundamentalcell

r

Max Reliability Configuration

A

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 11: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

11

Underlying WSNLogical WSN Deployment (Network

Topology)bull Dynamic Clustered Spanning Tree (DCST) It represents a design

assumption motivated byndash Cluster Heads (CHs) assigned on-demand (by a Cost Function)ndash Support to ldquodata centricrdquo applications (functions rarr data)ndash ldquoTable-lessrdquo routing protocolsndash Support to data aggregation fusion (at CHs)ndash Support the mobile agent propagation from CHs to their CMs

CH

CH

BS

CH

CH CH

BS

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 12: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

12

Underlying WSNPlanned Network Topology

bull Planned Network Topology (PNT graph) Defines the graph including the sub-set of DCSTs compliant to the specific constraints defined by the Planner (rarr admissible DCSTs)

ndash Each node knows its admissible neighborsbull How many DSCT in a given PNT Kirchhoffrsquos Theorem

N the nodes in the network lt σ gt average neighbors per node

1NN1

4

7 1

23

5

6

N = 7lt σ gt 34 220

σ(1) = 3σ(2) = 3σ(3) = 6σ(4) = 3σ(5) = 3σ(6) = 3σ(7) = 3

236

145

897

N = 9lt σ gt 44 15600

σ(1) = 3σ(2) = 5σ(3) = 8σ(4) = 5σ(5) = 3σ(6) = 5σ(7) = 3σ(8) = 3σ(9) = 5

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 13: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

13

WSN Topology Manager(ARCHEA)

A ARCHEA defines a Cost Function to elect CHs among a set of eligible nodes such that the resulting DCST is the shortest balanced DCST among the possible choices

Q Given a WSN physical deployment and a Planned Network Topology find the class of ldquoshortrdquo and ldquobalancedrdquo admissible DCSTs compliant to resource requirements

Route-Cost Quality Indicators

bull It includes the conditions to preserve spanning trees in WSN [Sec 52]bull It is shown [Sec 54] that the elected CH has minimum Hop Count (hCH) to sink and maximum number of

CM [σ(CH)] respect to the other eligible nodes (rarr balanced cluster sizes)

bull Short and balanced DCST It represents a design assumption motivated byndash Reduced code transmission hops (for mobile agent propagation)ndash Augmented reliability in data aggregation at CHsndash ARCHEA and routing messages can be crypto-secured

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 14: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

14

TAKSDriving Ideas amp Tools

Link layer Cryptography provides security against outsider intruders bull TAKS are symmetric pair-wise no pre-distributed (only key

components are pre-distributed)bull TAKS is deterministicbull TAKs are symmetric keys generated using asymmetric mechanisms

(hybrid cryptography)bull Network Topology Authentication as pre-condition for TAK generationbull Cryptographic Entropy per TAK binit 1 bit (for any TAK length)bull Certification Authority is distributed on nodes of the admissible

DCSTsbull Reverse engineering problem more complex than Discrete Logarithm

Problem (DLP)bull Cryptographic information is classified in public restricted

private secretbull Vector algebra on GF(q) with q = 2k and k the TAK length in binit

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 15: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

15

TAKSTopology Authentication

bull Network Topology Authentication as pre-condition for TAK generation

bull If the Planner is also Certifierndash Planned Network Topology rarr Certified Network Topologyndash Admissible DCST rarr Authenticated DCST

bull Node in an Authenticated DCST becomes local CA because it knows its admissible neighbors

ndash Centralized CA rarr Distributed CA

TAK can be generated in a node pair only if mutual authentication has been successful therefore the resulting DCST is both admissible and authenticated

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 16: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

16TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK = Keyi = Keyi

ni nj

TrKeyCompi

TrKeyCompj

TrKeyCompi

Node nj is authenticated

LocPldTopi

V(TrKey Compj LocPldTopi = 0

YESKeyi = f (LocKey Compi TrKey Compj)

YESKeyj = f (LocKeyCompi TrKeyCompj)

Node ni is authenticatedV(TrKeyCompi LocPldTopj = 0

external server

IntrusionDetectionSystem

NO

NO

LocKeyCompiTrKeyCompjLocPldTopj

LocKeyCompj

TAK Generation

TAK Authentication Theorem [Sec 641]

TAK Generation Theorem [Sec 642]

f() and V() [Sec 64]are public (Kerchoffrsquos principle)

privaterestrictedrestricted

Local Conf Data [Sec 64]

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 17: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

17

Security AnalysisQ Is TAK a real cryptographic key Ie which is the cryptographic entropy per binit associated to TAKA It is shown [Sec 651] that TAK Cryptographic Entropy per binit is asymp 1

Q How much a single node is secure Ie how much complex is the inverse problem to break TAK Generators from the cryptographic information available on a single node (security level in a single node) A It is shown [Sec 652] that is harder than Discrete Logarithm Problem

Q How much a network is secure Ie how many nodes should be compromised to derive TAK Generators from the cryptographic information available on the network (security level in the network)A It is shown [Sec 653] that TAKS scheme is N-secure

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 18: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

18

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz assuming 20 clock

cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale 312 416 MHz assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bitsqlog)2(3 2

TAK length

Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 4 )

128 bit 1400 5 ms 50 s 300 bytes 256 bit 13000 40 ms 400 s 600 bytes 512 bit 120000 370 ms 37 ms 1200 bytes

1024 bit 1100000 32 s 32 ms 2400 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 19: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

19

bull Intrusion Alarm Generation issues alarms according to a predefined Anomaly Detection Logic (ADL) and threat models

bull Intrusion Reaction Logic (IRL) defines the defence strategy (schedule of interventions) and tracks correlated alarms

bull Intrusion Reaction Logic Application (IRLA) reacts to intrusion by applying the suited countermeasures (link release putting compromised nodes in quarantine distributing black lists grey lists hellip)

Reference IDS Macro-functions

IntrusionAlarm

GenerationLocal

Conf DataIntrusionReaction

Logic

IntrusionReaction

Application

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 20: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

20

WPM-based IDSDriving Ideas amp Tools

IDS provides security against insider intrudersbull Incoming message Anomaly Rules Observablesbull Behavior is modelled using WPMbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash Deterministic vs stochastic observable-state relationshipsndash ldquo0-1rdquo reachability rules for observable-state relationships

bull Classification of WPM states according to their topological position within the WPM machine (eg LPA HPA states) and according to the associated ldquothreat observablesrdquo (eg UPA states)

bull Threat Observables States Traces Scores Alarm Countermeasuresbull WPM (Weak Process Model) vs HMM (Hidden Markov Model)

ndash ldquopossible states tracesrdquo vs ldquothe most probable states tracerdquo (Viterbi)

ndash Possible states traces are equi-probablebull Alarm generation when at least a states trace contains at least an HPA

ndash Scores (weights) associated to state traces

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 21: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

21

WPM-based IDS Micro-functions

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 22: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

22

WPM-based IDS Information Flow

DefenceStrategy

AnomalyDetection

Logic

ThreatModel

AlarmTracking

Countermeasure Application

LocalConf Data

Signalling IE

xkok

Al[sk]

cm(s)

Controlmessages

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 23: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

23

WPM-based Anomaly Detection Model

010010000000990000010009900100000

S

o6 = 3 1 4 2 5 6

al[01|01]

al[02|00]

1100

99

-100

-100

1100

-100

-100

99

-990

L = 1 H = 100

LPA

HPA

Score Matrix SScore Computation

WPM Algebraic Canonical Form

k=1 k=2 k=3 k=4 k=5 k=6

WPM States Traces

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 24: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

24

Threats from insider intruders

57CH

M

5 7

E

ni

nj

1

1CH

M

Eni

nj

1

1CH

M

33

Eni

nj

CH

M

1CH3

31

3

E

M

ni

nj nj

1E

ni1

3

low latencylink

HELLO Flooding SINKHOLE

inter-cluster WORMHOLEintra-cluster WORMHOLE

(HF) (SH)

(WH)

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 25: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

25

Examples of Anomaly RulesAR1 If nE has authenticated node nE node nE declares hE lt hi with hi ne 0 (in

other words node nE introduces itself as the new CH of ni but the current CH of ni is still alive) then ok = o1

AR2 If ni is CH and (rule AR1 or rule AR2) in nj is true then ok = o2This AR enables the ldquothreat observablesldquo back-propagation

AR3 If ni has authenticated node nE and node nE declares hE hi (in other words node nE introduces itself as a new cluster member M) then ok = o3This AR produces an ambiguous observable (Undecided Threat Obs)

The observable ok = o9 is produced if no observables for a sequence of K observation steps with K a predefined threshold

hellip

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 26: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

26

WPM-based Single Threats Models

HELLO Flooding

SINKHOLE

WORMHOLE(HF)

(SH)

(WH)

(9)

HF_5RESET

HF_6SUCCESSFULLYH FLOODING

99

-1

-100

(56)

HF_11

(65)

HF_3

99

-1

-100

(78)

HF_21

(87)

HF_4

(9)

SH_3RESET

SH_4SUCCESSFULLY

SINKHOLE

99

-1

-100

(12)

SH_11

(12)

SH_2

(9)

WH_5RESET

WH_6SUCCESSFULLY

WORMHOLE

99

-1

-100

(12)

WH_11

(34)

WH_3

99

-1

-100

(34)

WH_21

(12)

WH_4

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 27: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

27

Al[sk]

Al[sk ]

Al[sk ]Aggregated Threat Model (I)

Al[sk](9)

X_9RESET

X_10SUCCESSFULLY

THREAT

99 99

-1

-100

(12)99

(87)

X_8

(34)

X_3

99

(56)

X_51

(78)

X_61

X_4

(34)

X_21

(65)

X_7

(12)

X_11

99

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 28: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

28

8886678555586775

(HF)

21221112112221

(SH)

312213342342244

(WH)

Security Analysis

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(HF)

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

0

100

200

300

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(SH)

ATMSTM

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 29: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

29

1

3

2

E

E

3 1

4

3

15 0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

Security Analysis

1

3 E

E

3 1

4

3

15

E

21

6

1

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31obs

scor

e

(WH)

(WH)

(WH)

(WH)

(SH)

(SH)

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 30: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

30

(9)

X_9RESET

X_10SUCCESSFULLY

THREAT

991 990

-10

-1000

(12)990

(87)

X_8

(34)

X_3

990

(56)

X_510

(78)

X_610

X_4

(34)

X_210

(65)

X_7

(12)

X_110

990

-1001

Aggregated Threat Model (II)

UPA state

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 31: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

31

Cost Analysisbull MICA2 8-bit processor ATMega128L 74 MHz) and assuming 20

clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 3 s

bull IMOTE 32-bit processor PXA271Xscale312 416 MHz) and assuming 5 clock cycles per arithmetic logic operation the average computation time per 32-bit operation is 003 s (assuming a conservative 300 MHz clock)

bull Memory usage is bytes2n3WMLn

nWML Number of 32-bit

operations

Estimated computation

time (assuming

MICA2 motes)

Estimated computation

time (assuming

IMOTE motes)

Estimated memory usage

(assuming 10n )

50 30000 100 ms 1 s 350 bytes 100 60000 200 ms 2 s 400 bytes

1000 600000 2 s 20 s 1300 bytes

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 32: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

32

AGILLA MA-AEEbull AGILLA is a mobile agent-based MW running on TinyOSbull Inter-agent communication via Tuple Space (rarr threat obs aggregation) bull Agents migrates via MOVE or CLONE (rarr agent propagation across DCST)bull STRONG or WEAK agent migrationbull Neighbor List (rarr admissible neighbors according to PNT graph)

TinyOS

Node (11)

Tuplespace

Agilla Middleware

Agents

TinyOS

Node (21)

Tuplespace

Agilla Middleware

Agentsmigrate

remote accessNeighbor

ListNeighbor

ListMiddleware Services Middleware Services

migrate

clone

MA-

AEE

[source Fok C-L et al ldquoAgilla A Mobile Agent Middleware for Sensor Networksrdquo Tech Report WUCSE-2006-16 2006]

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 33: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

33

Enhanced AGILLA MA-AEE

Underlying WSN Deployment

Secure Platform

AGILLA MA-AEE

Agent-based Applications

SWcomponent

SWcomponent

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Agent A1 AgentA2 AgentAn

Localmemory

AGILLAservices

Tuple space

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 34: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

34

IDS Functions Mapping

IRA

DefenseStrategy

AnomalyDetection

Logic

AlarmTracking

Countermeasure Application

Controlmessages

ThreatModel

LCD

IDSCore comp IRA

IDSMA comp

Intrusion Reaction Agent

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 35: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

35

IRA forward-propagation vs

Threat Observables back-propagation

IRA

Al[s] ok

IRAclone

1AGILLA MA-AEE

4AGILLA MA-AEE

5AGILLA MA-AEE

Al[s] ok

Al[s] ok Al[s] okAl[s] ok

3AGILLA MA-AEE

6AGILLA MA-AEE

2AGILLA MA-AEE

IRAclone

This mechanism avoids the injections of new IRA instances from the sink

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 36: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

36

WINSOME PBD (II)

Underlying WSN Deployment

AGILLA MA-AEE

IRAMonitoring Applications

SensorNode

SensorNode

SensorNode

SensorNode

SensorNode

Integrity Monitoring

Agent

otheragents

AnomalyDetection

LogicThreatModel TAKS ARCHEA

Secure Platform

NetManagerLCDTuple Space

AGILLAservices

IDS core comp

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 37: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

37

Secure Platform internal Structure

AGILLA MA-AEEcm[s]

ok

al[sk]

al[sk]

Comms

NetManager

al[sk]

cm[s]

ok

Secure Platform

Control Msgs

Tuple Space

IRA

AGILLA MA-AEE

Remote Tuple Space

IRA

TMok

Hp_xk

ok

ADL

IRLIRLA

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 38: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

38

Next steps (near-term)bull Finalization of WINSOME components development

ndash on-going implementations of AGILLA enhancements bull 2 theses finalized 1 thesis in on-going

bull Extensions of WPM-based IDS to data messages ndash on-going jointly with UC Berkeley

bull Enhancements of WPM technique to reduce false positivesbull Extension of TAKS to cluster keys

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 39: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

39

Next steps (mid-term)

Anomaly Detectionapplied to sensed

data

Agent basedSW design

Further WPM-based

Threat Modeling

DetectionProcess

Threat Identification Mechanisms

Applications to Hybrid Systems

Control

MonitoringTheory

MWService SupportEnhancement

CooperativeCommunication

s

WINSOME Project

DefenceStrategies

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 40: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

40

Scientific Contributions[1]M Pugliese and F Santucci ldquoPair-wise Network Topology Authenticated

Hybrid Cryptographic Keys for Wireless Sensor Networks using Vector Algebrardquo in 4th IEEE International Workshop on Wireless Sensor Networks Security (WSNS08) Atlanta 2008

[2]M Pugliese A Giani and F Santucci ldquoA Weak Process Approach to Anomaly Detection in Wireless Sensor Networksrdquo in 1st International Workshop on Sensor Networks (SN08) Virgin Islands 2008

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 41: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

41

In preparationbull M Pugliese A Giani and F Santucci ldquoWeak Process Models for

Attack Detection in a Clustered Sensor Network using Mobile Agentsrdquo submitted to the 1st International Conference on Sensor Systems and Software (S-Cube 2009)

bull M Pugliese and F Santucci ldquoA Comprehensive Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

bull M Pugliese L Pomante and F Santucci ldquoAgent-based Design and Implementation of a Cross-Layer Framework for Secure Monitoring Applications based on WSNrdquo

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 42: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

42

AcronymsADL Anomaly Detection LogicAR Anomaly RulesAGILLA AGile bombILLAARCHEA Available Resource Cluster-Head Election AlgorithmDCST Dynamic Clustered Spanning TreeDLP Discrete Logarithm ProblemGF Galois FieldHMM Hidden Markov ModelHPA High Potential AttackIDS Intrusion Detection SystemIRA Intrusion Reaction AgentIRL Intrusion Reaction LogicLCD Local Configuration DataLPA Low Potential AttackTAKS Topology Authenticated Key SchemeTGMP TAK Generation Management ProtocolWINSOME WIreless sensor Network-based Secure system fOr

structural integrity Monitoring and alErtingWML WPM Memory LengthWPM Weak Process ModelWSN Wireless Sensor Network

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 43: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

43

Grazie per lrsquoAttenzione

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 44: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

44

BACKUP SLIDES

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 45: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

45

Underlying WSNPhysical WSN Deployment

bull Metricsndash Sensor Node Density (SND) It is defined as the ratio between

the number of sensor nodes and the aggregated coverage (supposing circular areas r = 1) generated by each sensor node

ndash Overlapping Detection Spot Percentage (ODSP) It is defined as the percentage of the aggregated overlapped coverage generated by all SND respect to the coverage area generated by a generic sensor node

bull Coverage-cost criteriandash Minimize SNDODSP to mimimize coverage redundancy for a

given SND ndash Minimize SNDODSP to maximize coverage reliability for a given

SND

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 46: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

46

Underlying WSNCoverage-cost Quality Indicators

bull The minimum for the product SNDODSP returns the physical node distribution which minimizes coverage redundancy (fundamental cell with SNs at the centre of an hexagon)

bull The minimum for the ratio SNDODSP returns the physical node distribution which maximizes coverage reliability (fundamental cell with SNs at the centre of an hexagon)

Fundamental Cell SND ODSP SNDODSP SND ODSP SNs at the centre of hexagon 033 034 011 097 SNs at the centre of square 033 072 024 046 SNs at the vertices of hexagon 036 234 084 015 SNs at the vertices of square 036 156 056 023

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 47: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

47

18

23

5

11

4

17

8

1

20

15

9

21

24

19

25

16 6 13 10 22

12 3 2 7 14

Underlying WSNDCST Deployment

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 48: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

48

11

4

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 49: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

49

11

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

18

23

5

16

12 3

4

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 50: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

50

17

8

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 51: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

51

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 52: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

52

17

1

20

15

9

21

24

19

25

6 13 10 22

2 7 14

16

12

5

18

23

3

4

11 8

Underlying WSNDCST Self-Organization

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 53: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

53

Underlying WSNRoute-cost Quality Indicators

ltCHgtThe ratio between the number of deployed Cluster Heads and the deployed nodes in the network

lt σ gt The ratio between the number of deployed neighbors per node and the number of logical ldquoplannedrdquo neighbors per node applied to the deployed Cluster Heads

lt h gt The ratio between the sum of all hop counts to reach all nodes in the network and the minimum hop count to reach the most distant ldquoplannedrdquo node applied to the deployed nodes in the network

ltCHgt ltσgt lthgt036 033 080

down 3 038 032 084down 3-4 039 031 087down 3-4-11 045 026 103down 3-4-11-8 048 025 117rearrange 12 048 025 110

DCST-Deployment

DCST-SO

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 54: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

54

bull Let GF(q) with q = 2k be an extended Galois Field where p is a prime and k is an integer for which q gtgt N where N is the total number of nodes in the network and q-1 has a suitable large prime divisor (1)

bull Let U be a vector field over GF(q) Any uU is a 3-pla where ux uy uz are elements in GF(q)

bull Let a function satisfying the following requirementsR1 Be a one-way function R2 must hold for

where is an arbitrary commutative operatorbull Let V() a 2-variable function satisfying the following requirements

R3 Be a one-way functionR4 V(vvrsquo) = 0 only for a particular sub-set of values vvrsquo V U

The explicit expressions for and are public (Kerchoffrsquos principle)

TAKS Definitions (12)f() and V()

(1) This restriction on the values for q is not mandatory but when applied any reverse engineering technique for TAK becomes harder

()f

const)u(f)u(f)u(f)u(f Uuu

()f ()V

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 55: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

55

a Let A U M U Elements in A are defined as follows a arsquo A if mM exists such that m(atimes arsquo) ne 0 A and M are secret

b Let b B GF(q) not a generator) in B B is secretc Let c C U C is secretd Let the explicit expression for where mM satisfied Ass (a)

and kGF(q) is an arbitrary constant The definition for f() is compliant to R1 and R2 because

for where (hereafter omitted) is the mod q product

e Let kl krsquol KL U (private) and kt krsquot KT U (restricted) be the LocKeyComp and TrKeyComp for node ni and nj respectively

f Let t T U (restricted) be the LocPldTop such that for the generic kt KT is tbullkt = 0

The explicit expressions for kl and kt are public (Kerchoffrsquos principle)

TAKS Definitions (22)Local Configuration Data

()mkb()f ()f

uum2uum2umumumum bkbkkbkbkbkb

Uuu )q(GFk

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 56: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

56

TAK Generation Theorem

2tjlii kkTAK

2tiljj kkTAK

kti kli

2)aa(m2ji aamkbTAKTAKTAK

ktj kljktj

kti

s = mf(a) srsquo = mf(arsquo)

askkba)a(fak

t

aml

askkba)a(fak

tj

amlj

()mkb()f

CBMA

cmbk

222ji aamafafTAKTAKTAK

For any f() compliant to R2

ni nj

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 57: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

57

TAK Authentication Theorem

In a node pair ni and nj if t σ(i) exists such that g(t ktj) = t bullktj = 0 then node nj is authenticated by node ni Viceversa if t σ(j) exists such that g(t kti) = t bullkti = 0 then node nj is authenticated by node ni (mutual authentication)

Suppose the pair ni-nj

bull LocPldTopi = σ(i) = ti where each vector ti (Topology Vector for node i) corresponds to each logical ldquoplannedrdquo neighbors for node ibull TAK authentication needs of

ndash TrKeyCompj vector ktj from node nj (the prover) ndash LocPldTopi vector t for node ni (the verifier) ndash If ktj ti = 0 then node nj is admissible ie included in the Planned Network Topology

bull The verification function g() is defined as the scalar product between t and kt this choice for g() is compliant to requirements R3 and R4

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 58: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

58

WPM-based Threat ModelDefinitions

bull States set is X = (x1 x2 hellip xn) X is n times 1bull Individual state x at step k is defined by xk = x1 x2 hellip xk xi X with

x0 (k=0) the initial statebull Observables set is O = (o1 o2 hellip oq) O is q times 1bull Individual observable at step k is defined by ok = o1 o2 hellip ok with oi

O

bull State Transition Distribution Matrix A (n times n) aij=1 if p(xk+1=xj|xk=xi)=1 aij=0 otherwise

bull Emission Distribution Matrix B (q times n) bij=1 if p(ok=oj|xk=xi)=1 bij=0 otherwise

bull Hypothetic Engaged States at step k is the sub-set of possible (hidden) states associated to the observable ok Hp_xk = BT ok

bull Hypothetic Free States at obs step i Free_xi = xi - (xi Hp_xi)bull Hypothetic States Trace at obs step k Trk =i=1 (xk A bull Free_xi)

k-1

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 59: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

59

WPM-based Threat Model Algebraic Canonical Form

0110000010010010100100000

A

100010100000110110100010010001

B

00001

x0

kk

k1k

BxoAxx

10000

xF

i-th column gives the states reachable from the i-th state i-th column gives the

observables of the i-th state

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 60: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

60

WPM-based Threat ModelGeneration of Hypothetic States Traces

x2=Ax1

o2 = 1x2 = 1

x2 = 5

x2=Bto2

x4=Ax3x3=Ax2

o1 = 3x1 = 4

x1 = 5

x1=Bto1

4

5

x6=Ax5x5=Ax4

3Tr1

6=1245Tr2

6=1351

5

o3 = 4x3 = 2

x3 = 3

x3=Bto3

o4 = 2x4 = 3

x4=Bto4

o5 = 5x5 = 4

x5=Bto5

o6 = 6x6 = 1

x6 = 5

x6=Bto6

2

34

1

5

k1k

kTk

AxxoBx

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 61: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

61

bull Assuming a WPM-based threat model (A B x0) 2 hazard levels for an attack can be defined

ndash Low Potential Attack (LPA) An attack is considered low potentially dangerousrdquo (or in a LPA state) if the threat is currently in a state xj which is at least 2 hops to the final state

ndash High Potential Attack (HPA) An attack is considered high potentially dangerous (or in a HPA state) if the threat is currently in a state xj which is 1 hop to the final state

bull Alarms al[sk] are issued when the attack has reached an HPA state

WPM-based Threat ModelHazard levels in an attack

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 62: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

62

WPM-based Threat ModelScore Matrix S

Score Computation Theorem [Sec ] states thatbull if node nj is an LPA state the total score in nj is sj = L bull if node nj is an HPA state the total score in nj is sj = H bull If node nj is neither LPA nor HPA or is a final state the total score in nj is sj = 0

and if

]nWMLint[log1010LH

ji)ss(ajiHL0s

jiijij

with and A State Transition Distribution matrix

klpa

khpa

k LnHns then

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 63: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

63

Hypothetic Free States (Free_xk)

kobs

WM

LThreat Score Computation

k

1i

iTi0T0kWMLk x_FreeSTrxSxx_Hps

WML

1i

iTi0T0kWMLkWMLkWMLk x_FreeSTrxSxx_Hpsss

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 64: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

64

Security Analysis Entropy associated to TAK

)k|k(H)k(H)kk(H jtiljtjtil qlog3 2

0)k(H jt

)k(H)k|k(H iljtil

Nqqlog3)k(H 2il

qlog)kk(H31H 2jtilTAK

Theorem on TAK Entropy TAK entropy per binit is asymp 1

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 65: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

65

Security AnalysisSecurity Level in a single node

The cryptographic robustness of TAK generation scheme is based on the difficulty on computing discrete logarithms (the well-known Discrete Logarithm Problem (DLP))

bull In this case the problem is harder to solve than the classic DLP because equations are not pure exponentials

amt

)ca(ml

bamkbak

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 66: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

66

Security AnalysisSecurity Level in a network

In case a node has been captured the following non-algebraic equations system should be solved for a m c b which are the secret components to generate all TAK keys in the network

amt

)ca(ml

amt

)ca(ml

bamkbak

bmsask

bak 6 equations (=3+3)10 variables (a m c b)

bull It can be shown that even capturing all N nodes in the network the attacker gets ~ q4 ndash Nq free solutions for (a m c b) which are still ~ q4 if is N ltlt q

bull Thus the scheme is N-secure

rarr ~ q4 solutions

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 67: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

67

WML

okHp_xkTuple Space

AG

NetManager

Comms

ok

ok

AR

Remote Tuple Space

al[sk]

ADL

TGMP msgsARCHEA msgs

TM

ADL Component

LCD

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 68: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

68

al[sk]

Hp_xk

Hp_xk

Free_xiltkFree_xk

Tuple Space

AG

WML

ScoreComputation

Trace Estimation

TM

AG sub-Component

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 69: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

69

Hp_xk

TM A B x0 xF

ok

AGAR

Remote Tuple Space

ADL

TM Component

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 70: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

70

NetManager Component

LCD

Comms

TGMP msgs RELEASE_ind(niCH)

cm[s]

Tuple Space

ARCHEA msgs

TAKS

ARCHEA msgs

ARCHEA msg

TGMP msgs

TGMP msgs

ARCHEA

NetManager

AR TAKgen okko

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 71: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

71

NetworkTopology

Authentication

KeyGeneration

LCD

klj

cm[s]Tuple Space

TAKS

ARCHEA

ktj σ(j)AR

TAKgen okko

ReplaceKeyRevokeKey

TinySec

Comms

TGM

P

TGMP msgs

TGMPmsgs

RELEASE_ind(niCH)

TAKS Component

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 72: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

72

Route CostComputation

ARCHEA

TAKS

Comms

ARCH

EA M

anag

er ARARCHEA

msgs

RELEASE_ind(niCH)

ARCHEA msgs

ARCHEA Component

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 73: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

73

TAK Gen Management Prot(TGMP)

nj

TAK-ciphered link TAK j=f (kljkti)

(1)

(2)

tjmiddot ktj=0

timiddot kti=0SETUP(kti)

SETUP(ktj)

TAK i=f (kliktj)

RELEASE(kti ktj)

ni

RELEASE_ind(niCH)ARCHEA

LCDi LCDj

TinySecRevokeKey(TAK)

TinySecReplaceKey(TAK)

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74
Page 74: Managing Security Issues in Advanced Applications of Wireless Sensor Networks

Doctorate Dissertation LrsquoAquila March 31st 2009

74

ARCHEA Protocol

ni nj

(1) EVALUATE_RC(hi σi)

ni=CH = minRCiUDPATE_H(hj)

RESET_H

UDPATE_H(hl)

UDPATE_H(hj)RESET_H

RESET_H

hj = hl+1

nl

(2)RELEASE_ind(niCH)TAKS

TAK-ciphered linkσi=AN[σ(ni)]hi

LCDihj

LCDj

  • Slide 1
  • Slide 2
  • Slide 3
  • Slide 4
  • Slide 5
  • Slide 6
  • Slide 7
  • Slide 8
  • Slide 9
  • Slide 10
  • Slide 11
  • Slide 12
  • Slide 13
  • Slide 14
  • Slide 15
  • Slide 16
  • Slide 17
  • Slide 18
  • Slide 19
  • Slide 20
  • Slide 21
  • Slide 22
  • Slide 23
  • Slide 24
  • Slide 25
  • Slide 26
  • Slide 27
  • Slide 28
  • Slide 29
  • Slide 30
  • Slide 31
  • Slide 32
  • Slide 33
  • Slide 34
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Slide 40
  • Slide 41
  • Slide 42
  • Slide 43
  • Slide 44
  • Slide 45
  • Slide 46
  • Slide 47
  • Slide 48
  • Slide 49
  • Slide 50
  • Slide 51
  • Slide 52
  • Slide 53
  • Slide 54
  • Slide 55
  • Slide 56
  • Slide 57
  • Slide 58
  • Slide 59
  • Slide 60
  • Slide 61
  • Slide 62
  • Slide 63
  • Slide 64
  • Slide 65
  • Slide 66
  • Slide 67
  • Slide 68
  • Slide 69
  • Slide 70
  • Slide 71
  • Slide 72
  • Slide 73
  • Slide 74