presented by Wei Dai

The iTrust Local Reputation System for Mobile Ad-Hoc

Networks

Overview1)Introduction2)The iTrust Search and Retrieval Network3)The iTrust Local Reputation System4)Experiments and Evaluation5)Conclusion and Future Work

Wei Dai WORLDCOMP - ICWN’13

Introduction Centralized search engines are prevalent

in today’s society Google, Yahoo!, Bing, etc. Censorship, filtering of information

Wei Dai WORLDCOMP - ICWN’13

Introduction iTrust is a decentralized information

search and retrieval network Addresses the problems of censorship and filtering

of information Distributes metadata and requests to random

participating nodes

Wei Dai WORLDCOMP - ICWN’13

The iTrust Search and Retrieval Network

Wei Dai WORLDCOMP - ICWN’13

The iTrust Search and Retrieval Network

Wei Dai WORLDCOMP - ICWN’13

The iTrust Search and Retrieval Network

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The iTrust Search and Retrieval Network

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The iTrust Search and Retrieval Network

Wei Dai WORLDCOMP - ICWN’13

iTrust is based on a hypergeometric distribution in terms of n, x, m, r, and k n: number of participating nodes

x: proportion of the n nodes that are operational

m: number of nodes to which the metadata are distributed

r: number of nodes to which the requests are distributed

k: number of participating nodes that report matches to a requesting node

The iTrust Search and Retrieval Network

Wei Dai WORLDCOMP - ICWN’13

The probability P(k ≥ 1) that a request yields one or matches is given by:

•

We found that if m = r =⌈2√n⌉, then P(k ≥ 1) ≥ 1 – e-4 ~ 0.9817, when x = 1.

Equation (1) and the above result provide the basis of our evaluation of the iTrust reputation system

The iTrust Search and Retrieval Network

Wei Dai WORLDCOMP - ICWN’13

iTrust is implemented over HTTP, SMS, and Wi-Fi Direct The iTrust reputation system focuses on the

mobile ad-hoc network using Wi-Fi Direct

The iTrust Local Reputation System The iTrust reputation system is designed to

combat subversive behavior of malicious nodes It does so while minimizing the expectation of

cooperation between nodes using local reputations based solely on direct observations of the nodes

Wei Dai WORLDCOMP - ICWN’13

The iTrust Local Reputation System Structured as Monitoring,

Reputation Rating, and Neighborhood Modules

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The iTrust Local Reputation SystemNeighborhood Module

Local neighborhood and reputation table Nodes within one hop are represented in

the reputation table Start with neutral reputation of zero

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The iTrust Local Reputation SystemMonitoring ModuleListens to neighbors’ transmissions, to ascertain whether nodes are unresponsive or forwarding messages improperly

Provides feedback to the Reputation Module

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A B C

Route: A -> B -> C

A B C1

2 2

11. 2.

The iTrust Local Reputation SystemReputation Rating ModuleReceives good/bad feedback from the Monitoring Module

+1/-2 Reputation, accordinglyBlacklisting, at -2 or -4Graylisting

Wei Dai WORLDCOMP - ICWN’13

The iTrust Local Reputation System

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Negative interaction [-2]

Previous reputation: -1 Current Reputation: -3

Positive interaction [+1]Previous reputation: -2 Current Reputation: -1

Negative interaction [-2]Previous reputation: 0 Current Reputation: -2

Positive interaction [+1]Previous reputation: N/A Current Reputation: 0

GRAYLISTED

BLACKLISTED

Experiments and Evaluation 150 Node Neighborhood

m: number of nodes to which metadata are distributed

r: number of nodes to which requests are distributed

1000 Node Network M: number of nodes to which metadata are distributed

R: number of nodes to which requests are distributed

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Experiments and Evaluation Simulations with 2 offense blacklisting 1000 node network, with 150 node neighborhood For the 1000 node network, we set M = 64, R = 64 For the 150 node neighborhood, to keep it

proportional, m = 9 ~ (64/1000) x 150 on average We experiment with different values of r

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Experiments and Evaluation

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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90.20.30.40.50.60.70.80.91.01.1 150 Nodes vs. 1000 Nodes [m = 9, r = 64 vs. M = 64, R = 64]

1000 Nodes150 Nodes

Ratio of Non-Malicious to Malicious Nodes

P( k

>=1

)

Experiments and Evaluation

Wei Dai WORLDCOMP - ICWN’13

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90.00.10.20.30.40.50.60.70.80.91.0

150 Nodes vs. 1000 Nodes [m = 9, r = 9 vs. M = 64, R = 64]

1000 Nodes150 Nodes

Ratio of Non-Malicious to Malicious Nodes

P( k

>=1

)

Experiments and Evaluation

Wei Dai WORLDCOMP - ICWN’13

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90.00.10.20.30.40.50.60.70.80.91.0

150 Nodes vs. 1000 Nodes [m = 9, r = 24 vs. M = 64, R = 64]

1000 Nodes150 Nodes

Ratio of Non-Malicious to Malicious Nodes

P( k

>=1

)

Experiments and Evaluation

Nodes Distribution Transmissions Blacklisted Remaining Proportion Blacklisted150 m = 9 10 0 30 0

r = 24 100 8 22 0.271000 25 5 0.83

10000 29 1 0.971000 M = 64 10 0 200 0

R = 64 100 0 200 01000 25 175 0.13

10000 182 18 0.91

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150 Nodes vs. 1000 Nodes [m = 9, r = 24 vs. M = 64, R = 64]

ConclusionSmaller local neighborhoods in the

iTrust reputation system effectively require fewer requests to detect malicious nodes Appropriate for mobile ad-hoc networks where high levels of interaction are rare

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Future WorkBase reputation ratings on user interactionsCombine reputation ratings and file rankings

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Questions? Comments? Website: http://itrust.ece.ucsb.edu

Contact information: Wei Dai: weidai@umail.ucsb.edu Yung-Ting Chuang: ytchuang@ece.ucsb.edu Isai Michel Lombera: imichel@ece.ucsb.edu

Our project is supported by NSF CNS 10-16193

Wei Dai WORLDCOMP - ICWN’13