1. presented by Wei Dai The iTrust Local Reputation System for Mobile Ad-Hoc Networks

2. Overview 1)Introduction 2)The iTrust Search and Retrieval Network 3)The iTrust Local Reputation System 4)Experiments and Evaluation 5)Conclusion and Future Work Wei Dai WORLDCOMP - ICWN’13

3. Introduction  Centralized search engines are prevalent in today’s society  Google, Yahoo!, Bing, etc.  Censorship, filtering of information Wei Dai WORLDCOMP - ICWN’13

4. 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

5. The iTrust Search and Retrieval Network Wei Dai WORLDCOMP - ICWN’13

6. The iTrust Search and Retrieval Network Wei Dai WORLDCOMP - ICWN’13

7. The iTrust Search and Retrieval Network Wei Dai WORLDCOMP - ICWN’13

8. The iTrust Search and Retrieval Network Wei Dai WORLDCOMP - ICWN’13

9. 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

10. 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

11. 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

12. 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

13. The iTrust Local Reputation System  Structured as Monitoring, Reputation Rating, and Neighborhood Modules Wei Dai WORLDCOMP - ICWN’13

14. The iTrust Local Reputation System  Neighborhood Module  Local neighborhood and reputation table  No des within one hop are represented in the reputation table  Start with neutral reputation of zero Wei Dai WORLDCOMP - ICWN’13

15. The iTrust Local Reputation System  Monitoring Module  Listens to neighbors’ transmissions, to ascertain whether nodes are unresponsive or forwarding messages improperly  Provides feedback to the Reputation Module Wei Dai WORLDCOMP - ICWN’13 A BC Route: A -> B -> C A BC 1 2 2 1 1.2.

16. The iTrust Local Reputation System  Reputation Rating Module  Receives good/bad feedback from the Monitoring Module  +1/-2 Reputation, accordingly  Blacklisting, at -2 or -4  Graylisting Wei Dai WORLDCOMP - ICWN’13

17. The iTrust Local Reputation System Wei Dai WORLDCOMP - ICWN’13 Negative interaction [-2] Previous reputation: -1Current Reputation: -3 Positive interaction [+1] Previous reputation: -2Current Reputation: -1 Negative interaction [-2] Previous reputation: 0Current Reputation: -2 Positive interaction [+1] Previous reputation: N/ACurrent Reputation: 0 GRAYLISTED BLACKLISTED

18. 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 Wei Dai WORLDCOMP - ICWN’13

19. 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 Wei Dai WORLDCOMP - ICWN’13

20. Experiments and Evaluation Wei Dai WORLDCOMP - ICWN’13

21. Experiments and Evaluation Wei Dai WORLDCOMP - ICWN’13

22. Experiments and Evaluation Wei Dai WORLDCOMP - ICWN’13

23. Experiments and Evaluation NodesDistributionTransmissionsBlacklistedRemainingProportion Blacklisted 150m = 9100300 r = 241008220.27 10002550.83 100002910.97 1000M = 641002000 R = 6410002000 1000251750.13 10000182180.91 Wei Dai WORLDCOMP - ICWN’13 150 Nodes vs. 1000 Nodes [m = 9, r = 24 vs. M = 64, R = 64]

24. Conclusion  Smaller 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 Wei Dai WORLDCOMP - ICWN’13

25. Future Work  Base reputation ratings on user interactions  Combine reputation ratings and file rankings Wei Dai WORLDCOMP - ICWN’13

26. Questions? Comments?  Website:  http://itrust.ece.ucsb.edu http://itrust.ece.ucsb.edu  Contact information:  Wei Dai: weidai@umail.ucsb.eduweidai@umail.ucsb.edu  Yung-Ting Chuang: ytchuang@ece.ucsb.eduytchuang@ece.ucsb.edu  Isai Michel Lombera: imichel@ece.ucsb.eduimichel@ece.ucsb.edu  Our project is supported by NSF CNS 10-16193 Wei Dai WORLDCOMP - ICWN’13