Building A Trustworthy, Secure, And Privacy Preserving Network

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Building A Trustworthy, Secure, And Privacy Preserving Network

Bharat BhargavaCERIAS Security CenterCWSA Wireless Center

Department of CS and ECEPurdue University

Supported by NSF IIS 0209059, NSF IIS 0242840 ,

NSF ANI 0219110, CISCO, Motorola, IBM

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Research Team

• Faculty Collaborators– Dongyan Xu, middleware and privacy– Mike Zoltowski, smart antennas, wireless security– Sonia Fahmy, Internet security – Ninghui Li, trust– Cristina Nita-Rotaru, Internet security

• Postdoc– Lezsek Lilien, privacy and vulneribility– Xiaoxin Wu, wireless security– Jun Wen, QoS– Mamata Jenamani, privacy

• Ph.D. students– Ahsan Habib, Internet Security– Mohamed Hefeeda, peer-to-peer– Yi Lu, wireless security and congestion control– Yuhui Zhong, trust management and fraud– Weichao Wang, security of ad hoc networks

More information at http://www.cs.purdue.edu/people/bb

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Motivation

• Lack of trust, privacy, security, and reliability impedes information sharing among distributed entities.– San Diego supercomputer center detected 13,000

DoS attacks in a three-week period [eWeek, 2003]– Internet attacks in February, 2004 caused an

estimated $68 billion to $83 billion in damages worldwide [British Computer Security Report]

– Business losses due to privacy violations• Online consumers worry about revealing personal data• This fear held back $15 billion in online revenue in 2001

– 52,658 reported system crashes caused by software vulnerabilities in 2002 [Express Computers 2002]

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Research is required for the creation of knowledge and learning in secure networking, systems, and applications.

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• Enable the deployment of security sensitive applications in the pervasive computing and communication environments.

Goal

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Problem Statement

• A trustworthy, secure, and privacy preserving network platform must be established for trusted collaboration. The fundamental research problems include:– Trust management– Privacy preserved interactions– Dealing with a variety of attacks and frauds in

networks– Intruder identification in ad hoc networks (focus

of this seminar)

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Applications/Broad Impacts

• Guidelines for the design and deployment of security sensitive applications in the next generation networks– Data sharing for medical research and treatment– Collaboration among government agencies for

homeland security– Transportation system (security check during travel,

hazardous material disposal)– Collaboration among government officials, law

enforcement and security personnel, and health care facilities during bio-terrorism and other emergencies

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Scientific Contributions

A. Trust formalization

B. Privacy preservation in interactions

C. Network tomography techniques for DoS attacks

D. Intrusion detection and intruder identification in ad hoc networks

E. Vulnerability analysis and threat assessment

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A. Trust Formalization• Problem

– Dynamically establish and update trust among entities in an open environment.

• Research directions– Handling uncertain evidence– Modeling dynamic trust– Formalization and detection of fraud

• Challenges– Uncertain information complicates the inference procedure.– Subjectivity leads to various interpretations toward the same

information.– The multi-faceted and context-dependent characteristics of trust

require tradeoff between representation comprehensiveness and computation simplicity of the trust model.

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Uncertain Evidence• Probability-based approach to evaluate the

uncertainty of a logic expression given a set of uncertain evidence– Atomic formula: Bayes network + causal

inference + conditional probability interpretation of opinion

– AND/OR expressions: rule defined by Jsang [Jsang'01]

– Subjectivity is realized using discounting operator proposed by Shafer [Shafer'76]

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Dynamic Trust• Trust production based on direct interaction

– Identify behavior patterns and their characteristic features

– Determine which pattern is the best match of an interaction sequence

– Develop personalized trust production algorithms considering behavior patterns

• Reputation aggregation– Global reputation vs. personalized reputation– Personalized reputation aggregation

• Determine the subset of trust information useful for a specific trustor by using collaborative filters

• Translate trust information into the scale of a specific trustor

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Trust Enhanced Role Assignment (TERA) Prototype

• Trust enhanced role mapping (TERM) server assigns roles to users based on – Uncertain & subjective evidence

– Dynamic trust

• Reputation server – Dynamic trust information repository– Evaluate reputation from trust information by using

algorithms specified by TERM server

Prototype and demo are available at

http://www.cs.purdue.edu/homes/bb/NSFtrust/

http://www.cs.purdue.edu/homes/bb/NSFtrust/

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TERA Architecture

T E R M s er v er

T E R M s er v er

T r u s t b as ed o n b eh av io r s

T r u s t b as ed o n b eh av io r s

R ep u ta tio n

R ep u ta tio n

R ep u ta tio n s er v er

Alic e

Bo b

T E R A

R o le r eq u es t

As s ig n ed r o le

R o le r eq u es t

As s ig n ed r o le

R BAC en h an c edap p lic a tio n s er v er

R BAC en h an c edap p lic a tio n s er v er

Us er 's b eh av io r

Us er 's b eh av io r

I n te r ac tio n s

I n te r ac tio n s

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Trust Enhanced Role Mapping (TERM) Server

• Evidence rewriting

• Role assignment– Policy parser – Request processor & inference engine– Constraint enforcement

• Policy base

• Trust information management– User behavior modeling – Trust production

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TERM Server

TERM

Credential Manager

Assign role

Credentials provided / retrieved

Role Assignment

Evidence statement

Evidence statement

Evidence Rewriting Trust toward

issuer

Trust toward user/issuer

Trust

Information Management

Behaviors

Policy Base

Role-assignment Policy

Role-assignment policies

Reputation

user

Reputation server

Policy maker

Application server

Trust information

Request role

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Fraud Formalization and Detection

• Model fraud intention– Uncovered deceiving intention– Trapping intention– Illusive intention

• Fraud detection– Profile-based anomaly detection

• Monitor suspicious actions based upon the established patterns of an entity

– State transition analysis• Build an automaton to identify activities that lead

towards a fraudulent state

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Model Fraud Intentions

• Uncovered deceiving intention– Satisfaction ratings

are stably low. – Ratings vary in a

small range over time.

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• Trapping intention– Rating sequence can

be divided into two phases: preparing and trapping.

– A swindler behaves well to achieve a trustworthy image before he conducts frauds.

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• Illusive intention– A smart swindler

attempts to cover the bad effects by intentionally doing something good after misbehaviors.

– Process of preparing and trapping is repeated.

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B. Private and Trusted Interactions

• Problem– Preserve privacy, gain trust, and control dissemination of data

• Research directions– Dissemination of private data– Privacy and trust tradeoff– Privacy metrics

• Challenges– Specify policies through metadata and establish guards as

procedures– Efficient implementation– Estimate privacy depending on who will get this information,

possible uses of this information, and information disclosed in the past

– Privacy metrics are usually ad hoc and customized

Detail slides at http://www.cs.purdue.edu/homes/bb/priv_trust_cerias.ppt

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Preserving Privacy in Data Dissemination

• Design self-descriptive private objects

• Construct a mechanism for apoptosis of private objects

apoptosis = clean self-destruction

• Develop proximity-based evaporation of private objects

• Develop schemes for data distortions

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Privacy-Trust Tradeoff• Gain a certain level of trust with the least loss of

privacy• Build trust based on digital credentials of users

that contain private information• Formulate the privacy-trust tradeoff problem• Estimate privacy loss due to disclosing a set of

credentials• Estimate trust gain due to disclosing a set of

credentials• Develop algorithms that minimize privacy loss

for required trust gain

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Privacy Metrics

• Determine the degree of data privacy– Size of anonymity set metrics– Entropy-based metrics

• Privacy metrics should account for:– Dynamics of legitimate users– Dynamics of violators– Associated costs

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Size of Anonymity Set Metrics

• The larger set of indistinguishable entities, the lower probability of identifying any one of them– Can use to ”anonymize” a selected private attribute value

within the domain of its all possible values

“Hiding in a crowd”

“More” anonymous (1/n)

“Less” anonymous (1/4)

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Dynamics of Entropy

• Decrease of system entropy with attribute disclosures (capturing dynamics)

– When entropy reaches a threshold (b), data evaporation can be invoked to increase entropy by controlled data distortions

– When entropy drops to a very low level (c), apoptosis can be triggered to destroy private data

– Entropy increases (d) if the set of attributes grows or the disclosed attributes become less valuable – e.g., obsolete or more data now available

(a)

(b)

(c) (d)

Disclosed attributes

H*

Allattribut

es

Entropy

Level

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Private and Trusted System (PRETTY) Prototype

(1)

[2a]

(3) User Role

[2b] [2d][2c1]

[2c2]

(2)

(4)

TERA = Trust-Enhanced Role Assignment

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Information Flow for PRETTY

1) User application sends query to server application.

2) Server application sends user information to TERA server for trust evaluation and role assignment.

a) If a higher trust level is required for query, TERA server sends the request for more user’s credentials to privacy negotiator.

b) Based on server’s privacy policies and the credential requirements, privacy negotiator interacts with user’s privacy negotiator to build a higher level of trust.

c) Trust gain and privacy loss evaluator selects credentials that will increase trust to the required level with the least privacy loss. Calculation considers credential requirements and credentials disclosed in previous interactions.

d) According to privacy policies and calculated privacy loss, user’s privacy negotiator decides whether or not to supply credentials to the server.

3) Once trust level meets the minimum requirements, appropriate roles are assigned to user for execution of his query.

4) Based on query results, user’s trust level and privacy polices, data disseminator determines: (i) whether to distort data and if so to what degree, and (ii) what privacy enforcement metadata should be associated with it.

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Experimental Studies

• Private object implementation– Validate and evaluate the cost, efficiency, and the impacts on the

dissemination of objects– Study the apoptosis and evaporation mechanisms for private objects

• Tradeoff between privacy and trust– Study the effectiveness and efficiency of the probability-based and

lattice-based privacy loss evaluation methods– Assess the usability of the evaluator of trust gain and privacy loss

• Location-based routing and services– Evaluate the dynamic mappings between trust levels and distortion

levels

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C. Tomography Research• Problem

– Defend against denial of service attacks – Optimize the selection of data providers in peer-to-

peer systems

• Research Directions– Stripe based probing to infer individual link loss by

edge-to-edge measurements– Overlay based monitoring to identify congested links

by end-to-end path measurement– Topology inference to estimate available bandwidth

by path segment measurements

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Defeating DoS Attacks in Internet

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Overlay-based Monitoring

• Do not need individual link loss to identify all congested links

• Edge routers form an overlay network for probing. Each edge router probe part of the network

• Problem statement– Given topology of a network domain, identify

which links are congested and possibly under attack

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Attack Scenarios

(a) Changing delay pattern due to attack

(b) Changing in loss pattern due to attack

Time (sec) Time (sec)

D

ela

y (

ms)

L

oss

Rati

o

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Identified Congested Links

(a) Counter clockwise probing (b) Clockwise probing

Probe46 in graph (a) and Probe76 in graph (b) observe high losses, which means link C4 E6 is congested.

Time (sec) Time (sec)

Loss

Rati

o

L

oss

Rati

o

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Probing: Simple Method

(a) Topology (b) Overlay (c) internal links

Congested link

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Analyzing Simple Method

• Lemma 1. If P and P’ are probe paths in the first and the second round of probing respectively, |P P’ | ≤ 1

• Theorem 1. If only one probe path P is shown to be congested in any round of probing, the simple method successfully identifies status of each link in P

• Performs better if edge-to-edge paths are congested

• The average length of the probe paths in the Simple method is ≤ 4

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Performance: Simple Method

Theorem 2. Let p be the probability of a link being congested in any arbitrary overlay network. The simple method determines the status of any link of the topology with probability at least 2(1-p)4-(1-p)7+p(1-p)12

Frac of actual congested links

Dete

ctio

n P

robabili

ty

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Advanced MethodAdvancedMethod()begin

Conduct Simple Method. E is the unsolved equation set

for Each undecided variable Xij of E do

node1 = FindNode(Tree T, vi, IN)

node2 = FindNode(Tree T, vj , OUT) if node1 ≠ NULL AND node2 ≠ NULL then

Probe(node1, node2). Update equation set E end if Stop if no more probe exists

endforend

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Analyzing Advanced Method

• Lemma 2. For an arbitrary overlay network with n edge routers, on the average a link lies on b = edge-to-edge paths

• Lemma 3. For an arbitrary overlay network with n edge routers, the average length of all edge-to-edge paths is d =

• Theorem 3. Let p be the probability of a link being congested. The advanced method can detect the status of a link with probability at least (1-(1-(1-p)d)b)

n

nn

log8

)23(

n

n

log2

3

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D. Intruder Identification in Adhoc On-demand Distance Vector (AODV)

• Problem – AODV are vulnerable to various attacks such as false

distance vector, false destination sequence, and wormhole attacks

– Detecting attacks without identifying and isolating the malicious hosts leaves the security mechanisms in a passive mode

• Challenges– Locate the sources of attacks in a self-organized

infrastructure– Combine local decisions with knowledge from other

hosts to achieve consistent conclusions on the malicious hosts

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Attacks on Routing in Mobile Ad Hoc Networks

Attacks on routing

Active attacks Passive attacks

Packet silent discard

Routing information hiding

Routing procedure

Flood network

False reply Wormhole attacks

Route request

Route broken message

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• Related Work– Vulnerability model of ad hoc routing protocols [Yang

et al., SASN ’03]– A generic multi layer integrated IDS structure [Zhang

and Lee, MobiCom ’00]– IDS combining with trust [Albert et al., ICEIS ’02]– Information theoretic measures using entropy

[Okazaki et al., SAINT ’02]– SAODV adopts both hash chain and digital signature

to protect routing information [Zapata et al, WiSe’03]– Security-aware ad hoc routing [Kravets et al,

MobiHOC’01]

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Ideas

• Monitor the sequence numbers in the route request packets to detect abnormal conditions

• Apply reverse labeling restriction to identify and isolate attackers

• Combine local decisions with knowledge from other hosts to achieve consistent conclusions

• Combine with trust assessment methods to improve robustness

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Introduction to AODV

• Introduced in 97 by Perkins at NOKIA, Royer at UCSB

• 12 versions of IETF draft in 4 years, 4 academic implementations, 2 simulations

• Combines on-demand and distance vector• Broadcast Route Query, Unicast Route Reply• Quick adaptation to dynamic link condition

and scalability to large scale network• Support multicast

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Route Discovery in AODV (An Example)

S

D

S1

S2

S3

S4

Route to the source

Route to the destination

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Attacks on AODV• Route request flooding

– query non-existing host (RREQ will flood throughout the network)

• False distance vector– reply “one hop to destination” to every request and select a

large enough sequence number

• False destination sequence number– select a large number (even beat the reply from the real

destination)

• Wormhole attacks– tunnel route request through wormhole and attract the data

traffic to the wormhole

• Coordinated attacks– The malicious hosts establish trust to frame other hosts, or

conduct attacks alternatively to avoid being identified

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Impacts of Attacks on AODV

Packet Delivery Ratio

Control packet / data packet

No Attacks 96% 0.38

Vicious Flooding 91% 2.93

False Distance 75% 0.38

False Destination Sequence

53% 0.66

Wormhole 61% 0.41

We simulate the attacks and measure their impacts on packet delivery ratios and protocol overhead

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False Destination Sequence Attack

D

S S1

S2 M

S3

RREQ(D, 3)

RREQ(D, 3)

RREQ(D, 3)

RREQ(D, 3)

RREP(D, 5)

RREP(D, 20)

Packets from S to D are sinking at M. Node movement breaks the path from S to M (trigger route rediscovery).

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During Route Rediscovery, False Destination Sequence Attack Is Detected

D

S S1

S2 M

S3

S4

RREQ(D, 21)

(1). S broadcasts a request that carries the old sequence + 1 = 21

(2) D receives the RREQ. Local sequence is 5, but the sequence in RREQ is 21. D detects the false desti-nation sequence attack.

Propagation of RREQ

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Reverse Labeling Restriction (RLR)

Blacklists are updated after an attack is detected.• Basic Ideas

• Every host maintains a blacklist to record suspicious hosts. Suspicious hosts can be released from the blacklist.

• The destination host will broadcast an INVALID packet with its signature when it finds that the system is under attack on sequence. The packet carries the host’s identification, current sequence, new sequence, and its own blacklist.

• Every host receiving this packet will examine its route entry to the destination host. If the sequence number is larger than the current sequence in INVALID packet, the presence of an attack is noted. The previous host that provides the false route will be added into this host’s blacklist.

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D

S S1

S2 M

S3

S4

BL {}

BL {S2}

BL {}BL {M}

BL {S1}

BL {}

D broadcasts INVALID packet with current sequence = 5, new sequence = 21. S3 examines its route table, the entry to D is not false. S3 forwards packet to S1. S1 finds that its route entry to D has sequence 20, which is > 5. It knows that the route is false. The hop which provides this false route to S1 was S2. S2 will be put into S1’s blacklist. S1 forwards packet to S2 and S. S2 adds M into its blacklist. S adds S1 into its blacklist. S forwards packet to S4. S4 does not change its blacklist since it is not involved in this route.

INVALID ( D, 5, 21, {}, SIGN )

Correct destination sequence number is broadcasted.

Blacklist at each host in the path is determined.

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D4

D1

S3

S1

M

D3

S4

S2

D2

M attacks 4 routes (S1-D1, S2-D2, S3-D3, and S4-D4). When the first two false routes are detected, D3 and D4 add M into their blacklists. When later D3 and D4 become victim destinations, they will broadcast their blacklists, and every host will get two votes that M is malicious host.

[M]

[M]

[M]

[M]

Hosts closer to malicious site are in blacklists of multiple hosts. In the above figure, M is in four blacklists.

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Combine Local Decisions with Knowledge from Other Hosts• When a host is destination of a route and is

victim by any malicious host, it will broadcast its blacklist.

• Each host obtains blacklists from victim hosts.

• If M is in multiple blacklists, M is classified as a malicious host based on certain threshold.

• Intruder is identified.• Trust values can be assigned to other hosts

based on past information.

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D3

M1

S1

D1

Routing topology Reverse labeling procedure

Multiple attackers exist in the network. More routes are under attack. When the false routes are detected, more blacklists will be broadcasted.

D2

M2 M3

S2 S3

D3

M1

S1

D1D2

M2 M3

S2 S3

Acceleration in Intruder Identification

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Reverse Labeling Restriction

• Update Blacklist by INVALID Packet• Next hop on the invalid route will be put into

local blacklist, a timer starts, and a counter increases. The time duration that the host stays in blacklist is exponential to the counter value.

• Labeling process will be conducted in the reverse direction of the false route.

• When timer expires, the suspicious host will be released from the blacklist and routing information from it will be accepted.

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Deal With Hosts in Blacklist

• Packets from hosts in blacklist• Route request: If the request is from suspicious

hosts, ignore it. • Route reply: If the previous hop is suspicious and

the query destination is not the previous hop, the reply will be ignored.

• Route error: will be processed as usual. RERR will activate re-discovery, which will help to detect attacks on destination sequence.

• INVALID: if the sender is suspicious, the packet will be processed but the blacklist will be ignored.

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Attacks of Malicious Hosts on RLR

• Attack 1: Malicious host M sends false INVALID packet• Because the INVALID packets are signed, it

cannot send the packets in other hosts’ name• If M sends INVALID in its own name

• If the reported sequence number is greater than the real sequence number, every host ignores this attack

• If the reported sequence number is less than the real sequence number, RLR will converge at the malicious host. M is included in blacklist of more hosts. M accelerated the intruder identification directing towards M.

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• Attack 2: Malicious host M frames other innocent hosts by sending false blacklist• If the malicious host has been identified, the

blacklist will be ignored• If the malicious host has not been identified, this

operation can only make the threshold lower. If the threshold is selected properly, it will not impact the identification results.

• Combining trust can further limit the impact of this attack.

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• Attack 3: Malicious host M only sends false destination sequence about some special host• The special host will detect the attack and

send INVALID packets.• Other hosts can establish new routes to the

destination by receiving the INVALID packets.

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Experimental Studies of RLR

• The experiments are conducted using ns2.• Various network scenarios are formed by

varying the number of independent attackers, number of connections, and host mobility.

• The examined parameters include:– Packet delivery ratio– Identification accuracy: false positive and

false negative ratio– Communication and computation overhead

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Simulation Parameter

Simulation duration 1000 seconds

Simulation area 1000 * 1000 m

Number of mobile hosts 30

Transmission range 250 m

Pause time between the host reaches current target and moves to next target

0 – 60 seconds

Maximum speed 5 m/s

Number of CBR connection 25/50

Packet rate 2 pkt / sec

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Experiment 1: Measure the Changes in Packet Delivery Ratio

Purpose: investigate the impacts of host mobility, number of attackers, and number of connections on the performance improvement brought by RLR

Input parameters: host pause time, number of independent attackers, number of connections

Output parameters: packet delivery ratioObservation: When only one attacker exists in the

network, RLR brings a 30% increase in the packet delivery ratio. When multiple attacker exist in the system, the delivery ratio will not recover before all attackers are identified.

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Increase in Packet Delivery Ratio: Single Attacker

X-axis is host pause time, which evaluates the mobility of host. Y-axis is delivery ratio. 25 connections and 50 connections are considered. RLR brings a 30% increase in delivery ratio. 100% delivery is difficult to achieve due to network partition, route discovery delay and buffer.

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X-axis is number of attackers. Y-axis is delivery ratio. 25 connections and 50 connections are considered. RLR brings a 20% to 30% increase in delivery ratio.

Increase in Packet Delivery Ratio: Multiple Attackers

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Experiment 2: Measure the Accuracy of Intruder Identification

Purpose: investigate the impacts of host mobility, number of attackers ,and connection scenarios on the detection accuracy of RLR

Input parameters: number of independent attackers, number of connections, host pause time

Output parameters: false positive alarm ratio, false negative alarm ratio

Observation: The increase in connections may improve the detection accuracy of RLR. When multiple attackers exist in the network, RLR has a high false positive ratio.

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Accuracy of RLR: Single Attacker

30 hosts, 25 connections 30 hosts, 50 connections

Host Pause time (sec)

# of normal hosts identify the attacker

# of normal hosts marked as malicious

# of normal hosts identify the attacker


0 24 0.22 29 2.2

10 25 0 29 1.4

20 24 0 25 1.1

30 28 0 29 1.1

40 24 0 29 0.6

50 24 0.07 29 1.1

60 24 0.07 24 1.0

The accuracy of RLR when there is only one attacker in the system

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Accuracy of RLR: Multiple Attackers

30 hosts, 25 connections 30 hosts, 50 connections

# of attackers # of normal hosts identify all attackers


# of normal hosts identify all attackers


1 28 0 29 1.1

2 28 0.65 28 2.6

3 25 1 27 1.4

4 21 0.62 25 2.2

5 15 0.67 19 4.1

The accuracy of RLR when there are multiple attackers

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Experiment 3: Measure the Communication Overhead

Purpose: investigate the impacts of host mobility and connection scenarios on the overhead of RLR

Input parameters: number of connections, host pause time

Output parameters: control packet overhead

Observation: When no false destination sequence attacks exist in the network, RLR introduces small packet overhead into the system.

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X-axis is host pause time, which evaluates the mobility of host. Y-axis is normalized overhead (# of control packet / # of delivered data packet). 25 connections and 50 connections are considered. RLR increases the overhead slightly.

Control Packet Overhead

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Research Opportunities: Improve Robustness of RLR

• Protect the good hosts from being framed by malicious hosts• The malicious hosts can frame the good hosts

by putting them into blacklist. • By lowering the trust values of both complainer

and complainee, we can restrict the impacts of the gossip distributed by the attackers.

• Adopting the trust management scheme proposed by Aberer and Despotovic [CIKM’01] to determine the lowering speed.

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• Avoid putting every host into blacklist• Combining the host density and movement

model, we can estimate the time ratio that two hosts are neighbors

• The counter for a suspicious host decreases as time passes

• Adjusting the decreasing ratio to control the average percentage of time that a host stays in the blacklist of another host

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• Defend against coordinated attacks• The behaviors of collusive attackers show

Byzantine manners. The malicious hosts may establish trust to frame other hosts, or conduct attacks alternatively to avoid being identified.

• Look for the effective methods to defend against such attacks. Possible research directions include:

• Apply classification methods to detect the hosts that have similar behavior patterns

• Study the behavior histories of the hosts that belong to the same group and detect the pattern of malicious behavior (time-based, order-based)

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An Architecture of Intruder Identification Agent

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• Intruder identification can be applied to detect more attacks in ad hoc networks:– DoS attacks– Malicious discard– Trust abuse and privacy violation

• Reverse labeling mechanism can be applied to identify the attackers that– Disseminate false routing information– Discard data packets– Generate gossip to destroy other hosts’

reputation

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Conclusions on Intruder Identification• False destination sequence attacks can be

detected by the anomaly patterns of the sequence numbers

• Reverse labeling method can reconstruct the false routing tree

• Isolating the attackers brings a sharp increase in network performance

• On going research will improve the robustness of the mechanism and the accuracy of identification

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Related Ongoing Research

A. Detecting wormhole attacks

B. Position-based private routing in ad hoc networks

C. Fault tolerant authentication in movable base station systems

D. Congestion avoidance routing in ad hoc networks

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Detecting Wormhole Attacks

• Problem statement The malicious nodes can eavesdrop the packets,

tunnel them to another location in the network, and retransmit them. This generates a false scenario that the original sender is in the neighborhood of the remote location.

wireless node 1

wireless node 2

attacker 1 attacker 2

tunnel

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• Research challenges– Detect wormholes when the malicious host can be

the legal member of the network– Control the overhead introduced by wormhole

detection to avoid the hosts being overwhelmed

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Classification of Wormholes• the wormholes are divided into 3 groups:

– Closed– Half open

– Open

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The Approach: End-to-End Mechanism• Assumption:

– The hosts have the positioning devices and loosely synchronized clocks

– Pair-wise keys have been deployed

• Ideas:– The source and the intermediate hosts will attach the

<time, position> pairs that record the receiving and forwarding events

– The attached information is protected by message authentication codes (MAC)

– The neighbor relation validations are conducted by the destination

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Validation at the Destination

• The MAC codes are calculated correctly

• The neighbor hosts are within the radio range when the packet is passed

• The average moving speed between the <time, position> pairs from the same host does not exceed the maximum value.

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• Divide the area into same-sized cells and the time into same-length slots

• Require a constant storage space and linear computation operations for every intermediate host

• Have a configurable wormhole detection capability

Controlling Overhead: Cell-based Open Tunnel Avoidance

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Computation Efficiency• The experiments are conducted on a iPAQ 3630

with 206M Hz CPU and 64M RAM• The computation overhead of wormhole

detection for one 10-hop route consumes less than 0.5% of its CPU.

• The computation resource of a real PDA can support wormhole detection using COTA without trouble.

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Conclusions

• The end-to-end mechanism can detect half open and open wormholes in ad hoc networks

• As a position information management scheme, COTA requires constant storage space and linear computation resource for every intermediate host

• The proposed mechanism can be adopted by real mobile devices

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B. Position-based Private Routing in Ad Hoc Networks

• Problem statement– To hide the identities of the nodes who are

involved in routing in mobile wireless ad hoc networks.

• Challenges– Traditional ad hoc routing algorithms depend

on private information (e.g., ID) exposure in the network.

– Privacy solutions for P2P networks are not suitable in ad hoc networks.

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Weak Privacy for Traditional Position-based Ad Hoc Routing Algorithm

• Position information of each node has to be locally broadcast periodically.

• Adversaries are able to obtain node trajectory based on the position report.

• Adversaries can estimate network topology.• Once a match between a node position and its

real ID is found, a tracer can always stay close to this node and monitor its behavior.

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AO2P: Ad Hoc On-Demand Position-based Private Routing• Position of destination is the information

exposed in the network for routing discovery.• A receiver-contention scheme is designed to

determine the next hop in a route.• Pseudo IDs are used instead of real IDs for data

packet delivery after a route is built up.• Route with a smaller number of hops will be

used for better end-to-end throughput.

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AO2P Routing Privacy and Accuracy

• Only the position of destination is revealed in the network for routing discovery. The privacy of the destination relies on the difficulty of matching a position to a node ID.

• Node mobility enhances destination privacy because a match between a position to a node ID is temporary.

• The privacy for the source and the intermediate forwarders is well preserved.

• Routing accuracy relies on the fact that at a specific time, only one node can be at a position. Since the pseudo ID for a node is generated from its position and the time it is at that position, the probability that more than one node have the same pseudo ID is negligible.

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Privacy Enhancement: R-AO2P

• The position of reference point is carried in rreq instead of the position of the destination.

• The reference point is on the extended line from the sender to the destination. It can be used for routing discovery because generally, a node that processes the rreq closer to the reference point will also process the rreq closer to the destination.

• The position of the destination is only disclosed to the nodes who are involved in routing.

Reference point in R-AO2P

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Illustrated Results

• Average delay for next hop determination

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Illustrated Results

• Packet delivery ratio

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Conclusions

• AO2P preserves node privacy in mobile ad hoc networks.

• AO2P has low next hop determination delay.

• Compared to other position-based ad hoc routing algorithm, AO2P has little routing performance degradation.

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C. Fault Tolerant Authentication in Movable Base Station System• Problem

– To ensure security and prevent theft of resources (like bandwidth), all the packets originating inside the network should be authenticated.

– Authentication may become unreliable when base station fails or node moves from one cell to another.

• Challenge– How to design fault tolerant authentication

methods that are robust in the above conditions– How to design the protocols adaptable and re-

configurable

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Proposed Schemes

• We propose two schemes to solve the problem.

– Virtual Home Agent– Hierarchical Authentication

• They differ in the architecture and the responsibilities that the Mobile Nodes and Base Stations (Agents) hold.

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Virtual Home Agent Scheme

VHA ID = IP ADDRESSMaster Home Agent (MHA) Database Server

Shared SecretsDatabase

Backup Home Agents Other nodes in the network

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Advantages of Proposed Scheme

• Has only 3 states and hence the overhead of state maintenance is negligible.

• Very few tasks need to be performed in each state (outlined in the tech report).

• Flexible – there could be multiple VHAs in the same LAN and a MHA could be a BHA for another VHA, a BHA could be a BHA for more than one VHA at the same time.

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Disadvantages of Virtual HA Solution

• Not scalable if every packet has to be authenticated– Ex: huge audio or video data

• BHA (Backup Home Agents) are idle most of the time (they just listen to MHA’s advertisements.

• Central Database is still a single point of failure.

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Hierarchical Authentication Scheme

• Multiple Home Agents in a LAN are organized in a hierarchy (like a tree data structure).

• A Mobile Node shares a key with each of the Agents above it in the tree (Multiple Keys).

• At any time, highest priority key is used for sending packets or obtaining any other kind of service.

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A

CB

GFED

K2

K1

(K1, P1)(K2, P2)

Database

Database

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Key Priority depends on several factors and computed as cumulative sum of weighted priorities of each factors:

Example Factors:• Communication Delays• Processing Speed of the Agents• Key Usage• Life Time of the Key

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D. Congestion Avoidance Routing in Ad Hoc Networks• Objective

– To bring the consideration of congestion in the design of the routing protocols.

• Thrust– To avoid congestion by minimizing contention for

channel access.

• Challenges– The global coupling effect of wireless channel access

in ad hoc networks.– Quantification of congestion without exchanging

messages with neighbors.

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Intermediate Delay (IMD)

• IMD is a routing metric that characterizes the impacts of channel contention, the length of the route, and the traffic load at individual nodes.

• IMD estimates the delay introduced by the intermediate nodes along the route using the sum of delays from each node.

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Ad Hoc Routing Based on IMD

B

A C

D E

F

H I

G

J

2P/C 2P/C

P/CPC

P/C

P/CP/C

Simplification of delay computation:

1. If channel capacity is C and packet size is P, delay is P/C.

2. If n nodes are in contention for a channel, each node gets C/n share of the channel capacity. The delay is nP/C.

Adapt to changes in traffic and network topology

B

A C

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Delay Estimation

• A mobile node is modeled as a single server queuing system.

• Total delay includes the delay for transmitting a packet and the delay in the queue.

• The key is to estimate the delay for transmitting a packet.– Node with active traffic

• Use the mean value to estimate the delay.

– Node without active traffic• Study the procedure of packet transmission to

obtain the expectation of the delay.

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IEEE 802.11 DCF (Distributed Coordination Function)

E[Tsucc]=TRTS+TCTS+TDATA+TACK+3TSIFS+E[Tbackoff]

E[Tfail]=TRTS+Ttimeout+E[Tbackoff]

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SAGA: Self-Adjusting Congestion Avoidance Routing Protocol

• SAGA is a distance vector routing protocol.– use IMD instead of hop count as the distance– bypass hop spots where contention is intense

• Lazy route query uses special route advertisement for local route discovery.

• Approach to reduce the oscillation of IMD and prevent a node from switching back and forth among alternative routes.

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Experimental Evaluation• Objective

– Study the performance of SAGA, AODV, DSR, and DSDV under congestion.

• Performance metrics– Throughput, delivery ratio, protocol overhead, and

end-to-end delay• Method

– Simulation using the network simulator ns2– Two types of UDP traffic: constant bit rate (CBR) and

pareto on/off (POO)– The offered traffic load is taken as the input parameter– Six experiments by varying the maximum speed of

movement of nodes and the number of connections– Five independent runs with random scenarios for each

experiment

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30 CBR Connections, Low Mobility (4m/s)

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10 POO Connections, High Mobility (20m/s)

Documents

Building A Trustworthy, Secure, And Privacy Preserving Network