Intrusion Detection for Wireless Sensor Networks

Intrusion Detection for Wireless Sensor Networks

Qualifying Exam28th April 2005

Presented by Edith NgaiSupervised by Prof. Michael R. Lyu

Outline

• Background

• Research direction

• Intrusion detection for WSN

• Tracing network attacks

• Conclusion & Proposed future work

Technology trend

• Small integrated devices• Smaller, cheaper, more powerful

• PDAs, mobile phones

• Many opportunities, and research areas• Power management

• Distributed algorithms

Wireless sensor networks

• Wireless sensor node• power supply

• sensors

• embedded processor

• wireless link

• Many, cheap sensors• wireless easy to install

• intelligent collaboration

• low-power long lifetime

Possible applications

• Military• Asset monitoring and management, battlefield

surveillance, biological attack detection

• Ecological• fire detection, flood detection, agricultural uses

• Health related• Medical sensing, microsurgery

• General engineering• car theft detection, inventory control, residential

security

Requirements

• Low energy use

• Efficient use of small memory

• In-network data processing• large amounts of raw data

• limited power and bandwidth

• Efficient data routing

• Node localization

WSN vs MANET

WSN MANET

Goal Detection / estimation of some events of interest Simply communications

Communication pattern Specialized to:Many-to-oneOne-to-manyLocal communications

Typically support routing between any pair of nodes

Energy/resources constrained

More Less

Mobility Mostly not mobile Mostly mobile

Cooperation among nodes

More cooperative, exhibit trust relationships Less cooperative

Security mechanism Authentication and routing based on public key cryptography is too expensive

Both public key or asymmetric cryptography can be applied

Routing Distance vector and source routing protocols are generally too expensive

Support different types of routing protocols

Security in WSN

• Main security threats in WSN are:• Radio links are insecure – eavesdropping /

injecting faulty information is possible

• Sensor nodes are not temper resistant – if it is compromised the attacker obtains all security information

• Protecting confidentiality, integrity, and availability of the communications and computations

Why security is different?

•Sensor Node Constraint

•Battery

•CPU power

•Memory

•Networking Constraints and Features

•Wireless

•Ad hoc

•Unattended

Network defense

Protect - Encryption - Firewalls - Authentication - Biometrics

Detect - Intrusions - Attacks - Misuse of Resources - Data Correlation - Data Visualization - Malicious S/W - Network Status/

Topology

React - Response - Terminate Connections - Block IP Addresses - Containment - Fishbowl - Recovery - Reconstitute

What is intrusion detection?

• Intrusion detection is the process of discovering, analyzing, and reporting unauthorized or damaging network or computer activities

• Intrusion detection discovers violations of confidentiality, integrity, and availability of information and resources

• Intrusion detection demands:• As much information as the computing

resources can possibly collect and store

• Experienced personnel who can interpret network traffic and computer processes

• Constant improvement of technologies and processes to match pace of Internet innovation

What is intrusion detection?

How useful is intrusion detection?

• Provide digital forensic data to support post-compromise law enforcement actions

• Identify host and network misconfigurations• Improve management and customer

understanding of the Internet's inherent hostility

• Learn how hosts and networks operate at the operating system and protocol levels

Intrusion detection models

• All computer activity and network traffic falls in one of three categories:

• Normal

• Abnormal but not malicious

• Malicious

• Properly classifying these events are the single most difficult problem -- even more difficult than evidence collection

Intrusion detection models

• Two primary intrusion detection models• Network-based intrusion detection monitors

network traffic for signs of misuse

• Host-based intrusion detection monitors computer processes for signs of misuse

• So-called "hybrid" systems may do both• A hybrid IDS on a host may examine network

traffic to or from the host, as well as processes on that host

IDS paradigms

• Anomaly Detection – look for abnormal

• Misuse Detection – pattern matching

• Burglar Alarms - policy based detection

• Honey Pots - lure the hackers in

• Hybrids - a bit of this and that

Anomaly detection

• Goals:• Analyze the network or system and infer what

is normal

• Apply statistical or heuristic measures to subsequent events and determine if they match the model/statistic of “normal”

• If events are outside of a probability window of “normal”, it generates an alert

Anomaly detection (cont)

• Typical anomaly detection approaches:• Neural networks - probability-based pattern

recognition

• Statistical analysis - modeling behavior of users and looking for deviations from the norm

• State change analysis - modeling system’s state and looking for deviations from the norm

Misuse detection

• Goals:• Know what constitutes an attack

• Detect it

• A database of known attack signatures should be maintained

Misuse detection (cont)

• Typical misuse detection approaches:• “Network grep” - look for strings in network

connections which might indicate an attack in progress

• Pattern matching - encode series of states that are passed through during the course of an attack

• e.g.: “change ownership of /etc/passwd” -> “open /etc/passwd for write” -> alert

Research Direction

Intrusion Detection for WSN

Types of attack

• Physical attack• Physical damage, destroy, tamper

• MAC layer attack• Jamming

• Network layer attack• Misdirection on routing• Selective forwarding• Sinkhole attack• Wormhole attack• Sybil attack• Rushing attack• Hello flood attack

• Application layer attack• Denial of service

Research proposal

Intrusion Detection

Anomaly DetectionPattern Reognition

...

Intrusion Tracing

Route TrackingTopology Overview

Attack Analysis...

Intrusion ReactionNode Isolation

Certificate Revocation...

Audit DataSensing Results Routing InformationNode Behaviors Network Topology

Data CollectionLocalization Data Fusion Routing

Behavior Monitoring History Recording Intrusion Detection Framework

Attack TypesDetected

AttackerLocated

Audit data

• Application data from sensors

• Routing information

• Node behavior record

• Network topology

Data collection

• Localization

• Data fusion

• Routing

• Behavior monitoring

• History recording

Procedures

• Intrusion Detection• Discover suspicious activity from audit data

• Detect the intrusions

• Classify the type of intrusions

• Intrusion Tracing• Trace of source of intrusions

• Identify and locate the intruders

• Intrusion Reaction• Resist to the intrusions

• Defend against further intrusions

Intrusion Detection in WSN

Network model

•BSj: base station at location (Xj, Yj)

•Si: sensor node at location (xi, yi)

•R: transmission range of the base station

•r: transmission range of the sensor node

•k-coverage: a node covers by k BSs

Definitions

• Coverage of a base station

• Number of coverage from base stations

• p sends data to q successfully (in 1-hop)

• p sends data to q successfully via k hops

• p fails in sending data from p to q

}:{ RBSppC ii

}1|...{ 2 NiBSiBSiBSipS jkik

Gqprqpqp s ,

):|},...,1{,(

|,..., 112

1111

qpppppjikji

qpppppGppqp

iiji

ski

si

ki

skk

s

qtopfromontransmissionfailureqp f ______

Types of intrusions

• Sinkhole SH(q), HelloFlood HF(q)• A region of nodes will forward packets

destined for a BS through an adversary

• Wormhole WH(q)• An adversary tunnels messages received in

one part of the network over a low latency link and replays them in a different part

Types of intrusions

• Missing Data MD(Ci)• Missing data from p to BSi

• Wrong Data (local) WDL(p)• Inconsistent data

• Selective Forwarding / Interference• Sensor p does not forward data to its

neighboring nodes

iif CpBSp |

mis

iw dBSpNdBSpd ))(()(

)( pNp f

Architecture

History

Route Tracing

Data Fusion (local,global)

TopologyNeighboringMonitoring Data Collection

RoutingMissing Data?

Inconsistent Data?

Intrusion Type Identification

Yes

Yes

Intrusion Location

Intrusion Reaction

Suspicious Behavior?

Yes

Suspicious Routes?

Yes

Intrusion detection components

• Data fusion• Local – neighboring nodes

• Global – overlapping areas

• Topology discovery

• Route tracing

• History

• Neighbor monitoring • Watchdog

Intrusion detection

Components\Attack Types I II III IV V

Neighbor Monitoring

BS Dominating intermediate node

Dominating intermediate node

Selective forwarding

N/A N/A

Sensor N/A N/A Selective forwarding

N/A Interference (jamming with neighbors)

Data Fusion Global (may have missing or inconsistent data)

(may have missing or inconsistent data)

Missing data Inconsistent data (IVa – malicious sensor or intermediate nodes)

Missing data

Local (may have missing or inconsistent data)

(may have missing or inconsistent data)

Missing data Inconsistent data (IVb – sensor failure or being compromised)

Missing data

Routing (with topology info.)

BS a region of nodes forward packet through the same adversary

An adversary tunnels messages and replays them in a different part

N/A N/A N/A

Attack Types: I - Sinkhole, Hello Flood II – Wormhole III – Missing DataIV – Wrong Data V - Interference

Intrusion Tracing in WSN

Related work

• IP Traceback in traditional network• Packet marking

• ICMP traceback message

Related work

• “dead” node• cases sending or routing measurement as died

• “silent” node• Ceases sending but status not determined

Tracing sinkhole attack

• Adversary lures nearly all traffic from a particular area through a compromised node

• Attracts network traffic by advertising a high quality path to the BS

• Common kind of violation is

selective forwarding1

Attack region detection

• The BS can detect the list of nodes affected by the intrusions• Missing data

• Inconsistent data

• Circle the attack area

Probing

• Collect the next hop, hop counts from the nodes in the affected area

1. At the beginning of a suspicious sinkhole attack occurs

BS -> N(x): <probing, BSi>

2. When a probing message is received from N(x)

x -> y (neighbors of x): <probing, x, BSi>

3. When node y receives a probing messagey -> x: <y, shortest_next_hop, shortest_hop_count>

(routing information to BS)

y -> y’ (neighbors of y): <probing, y, BSi>

4. The processes (3) repeats until the request messages reach the boundary of the attack area

2

2

2

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3

34

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4

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344

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21

SH 2

Identify the sinkhole• Sinkhole does not have

outgoing edges

• Incoming edges to sinkhole should provide minimum no. of hop counts to BS

2

2

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34

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4

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1SH 2

Search from the leaf nodes

to the root (Sinkhole)

1

3

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33

3

2

2

1A

B

SH’

SH

(a)

(c)

(b)

(d)

SH’

SH

C

D

E

F

GH

With colluding nodes

Attack area with colluding nodes (a) missing information (b) cycles(c) misleading sinkhole (d) identification sinkhole using hop counts

Missing information

Misleading Sinkhole

Routing loop

Wrong routing information

Enhanced algorithm

• Finding array on hop countsfor each root r

initialize a new array countcheckRootByCount(r, count, 1);if (count[0] => numNode(r)/2)

r is a correct root.end if

end for

checkRootByCount (Node r, Array count, int depth)depth = depth +1for each precedent node p of r

increase count[ w(p,r) – depth ] by 1 checkRootByCount (p, count, depth)

end forend checkRootByCount

h -2 -1 0 1 2

Count[h] 0 0 n 0 0

Calculate the array “Count”

Call method “checkRootByCount”

for each roots

Enhanced algorithmfor each root r

initialize a new Array countinitialize a new Path correctPath

checkRootByCount(r, count, 1)

S = {x>0 | forall y>0, count[x]+count[-x]>count[y]+count[-y]}x = min (S)

correctRoot(r, r, x, 0, correctPath , count[0])apply correctPath on Network G

end for

correctRoot (Node r, Path p, int totalLevel, int currentLevel, Path correctPath, int bestCount)

if (currentLevel >= totalLevel)return

end if

currentLevel= currentLevel+1for each precedent node c of r

initialize a new Array countreverse edge (c,r)

checkRootByCount (c, count, 1)if (count[0]> bestCount)

correctPath = p->cend ifcorrectRoot(c, p->c, totalLevel, currentLevel, correctPath , bestCount)

reverse edge(c,r)end for

end correctRoot

Correct the root by specifying another suspicious Sinkhole

Calculate the array “Count” again

Calculate no. of hop counts for

correction

Select the best result

2

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22

1A

SH’

SH

Example – Before correction

i -2 -1 0 1 2

Count[i] 0 14 8 6 0

1

Value provided by node X

= 4

Deduced value from X to SH’

= 3

Count of node X

= 4 – 3 = 1

(=>SH should be 1 hop farther away than SH’)

X

Value provided by node Y

= 3

Deduced value from Y to SH’

= 4

Count of node Y

= 3 – 4 = -1

(=>SH should be 1 hop closer than SH’)

Y

Example – After correction

i -2 -1 0 1 2

Count[i] 0 1 21 6 0

Value provided by node X

= 4

Deduced value from X to SH’

= 4

Count of node X

= 4 – 4 = 0

(=>hop count agrees with SH)

X

Value provided by node Y

= 3

Deduced value from Y to SH’

= 3

Count of node Y

= 3 – 3 = 0

(=>hop count agrees with SH)Y2

3

2

3

3

34

4

4 4

4

4

34

2 3

3

4

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344

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2

1A

SH’

SH

Conclusion & Proposed Work

Required technologies

• Collection of the audit data• Localization• Data fusion• Routing

• Analysis on the audited data• Identifying the intrusion characteristics• Detecting the intrusions• Locating the intrusions

• Intrusion reaction

Proposed work

• Study how to collect the audit data effectively and complete the intrusion detection architecture

• Investigate the methods to analyze the audited data for intrusion detection

• Propose new methods to identify and locate the intruders (for various attacks)

• Study and explore reactive measures to defend against the detected intrusions

• Formulate and evaluate our intrusion detection framework which is expected to be effective in detecting and resisting to the many types of intrusions

Conclusion

• We discussed the characteristics of WSN and its security issues

• We studied traditional intrusion detection technologies

• We introduced our intrusion detection framework in our research proposal

• We proposed an intrusion detection architecture and analyzed some kinds of intrusions can be detected

• We proposed an algorithm for tracing Sinkhole attack for WSN

• We presented our proposed future work

Q & A