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The iTrust Local Reputation System for Mobile Ad-Hoc Networks

The iTrust Local Reputation System for Mobile Ad-Hoc Networks. presented by Wei Dai. Overview. Introduction The iTrust Search and Retrieval Network The iTrust Local Reputation System Experiments and Evaluation Conclusion and Future Work. Wei Dai . WORLDCOMP - ICWN’13. Introduction .

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The iTrust Local Reputation System for Mobile Ad-Hoc Networks

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

  2. Overview • Introduction • The iTrust Search and Retrieval Network • The iTrust Local Reputation System • Experiments and Evaluation • 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 • 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 Wei Dai WORLDCOMP - ICWN’13

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

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

  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 • Nodes 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 Route: A -> B -> C 1. 2. 1 1 C B A C B A 2 2 Wei Dai WORLDCOMP - ICWN’13

  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 GRAYLISTED BLACKLISTED Wei Dai WORLDCOMP - ICWN’13

  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 150 Nodes vs. 1000 Nodes [m = 9, r = 24 vs. M = 64, R = 64] Wei Dai WORLDCOMP - ICWN’13

  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 • 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

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