1. Fuzzy Trust Recommendation Based on Collaborative Filtering for Mobile Ad-hoc Networks Junhai Luo 1,2, Xue Liu 1, Yi Zhang 3,Danxia Ye 2,Zhong Xu 1 1 McGill University 2 University of Electronic Science and Technology of China 3 University of California September 2008 10/1/20151

2. Outline Motivations Related Work Architecture Algorithm Realization Performance Evaluation Conclusion and Future Work 10/1/20152

3. Motivations MANETs characteristics: ◦ Cooperative ◦ Autonomous ◦ Self-organized 10/1/20153 S D i j

4. Motivations(cont.) ◦ Low power ◦ Multi-hop ◦ Vulnerable to various attacks 10/1/20154 PDA Pen computer Laptop computer PDA

5. Motivations(cont.) 1) High trust value = ？ High or correct recommendation to other nodes. 2) Uncertain 10/1/20155 Why ？

6. Motivations(cont.) Methods ◦ Collaborative filtering ◦ Fuzzy logic 10/1/20156

7. Related Work CONFIDANT [1] ◦ DSR (Dynamic Source Routing) with reputation system NUGLETs [2] ◦ Virtual currency SORI [3] ◦ Secure and objective reputation scheme CORE [4] ◦ Collaborative observations and reputation mechanism [1] S. Buchegger and J.-Y. L. Boudec, Performance analysis of the confidant protocol,” in MobiHoc ’02: Proceedings of the 3rd ACM international symposium on Mobile ad-hoc networking & computing. New York, NY, USA: ACM, 2002, pp. 226–236 [2] L. Buttyan and J.-P. Hubaux, “Nuglets: a Virtual Currency to Stimulate Cooperation in Self-Organized Mobile Ad Hoc Networks,” Tech. Rep., 2001 [3] Q. He, D. Wu, and P. Khosla, “Sori: a secure and objective reputation based incentive scheme for ad-hoc networks,” Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE, vol. 2, pp. 825–830 Vol.2, 21-25 March 2004. [4] P. Michiardi and R. Molva, “Core: a collaborative reputation mechanism to enforce node cooperation in mobile ad-hoc networks,” in Proceedings of the IFIP TC6/TC11 Sixth Joint Working Conference on Communications and Multimedia Security. Deventer, The Netherlands, The Netherlands: Kluwer, B.V., 2002, pp. 107–121. 10/1/20157

8. Architecture 8 ij2j2 jKjK j1j1 … K R i R jk,m R j R i,m m

9. AlgorithmRealization Algorithm Realization Local trust Value Collaborative filtering Fuzzy trust recommendation 10/1/20159

10. Algorithm Realization(cont.) Local Trust Value ◦ Neighbor monitoring [3] 10/1/201510 Number of packets forwarded by node m Number of packets Requested for Forwarding by node j

11. Algorithm Realization(cont.) Collaborative Filtering Collaborative Filtering ◦ Similarity Functions  Cosine-Based Similarity  Correlation-Based Similarity  Adjusted Cosine Similarity 10/1/201511

12. Algorithm Realization(cont.) Fuzzy Method ◦ Fuzzy Membership Function ◦ Fuzzy Levels ◦ Fuzzy Inference 10/1/201512

13. Algorithm Realization(cont.) Fuzzy Membership Function ◦ Trapezoid Membership Function (TMF) 10/1/201513 a1a1 a2a2 a3a3 a4a4 1 0 Trust Levels Degree

14. Algorithm Realization(cont.) Fuzzy Levels 10/1/201514 Trust levelDescription Trapezoid Membership Function HDHigh Distrust[-1, -0.8, -0.6] DDistrust[-0.8, -0.6, -0.4,-0.2] UDUndistrust[-0.4, -0.2, 0] UTUntrust[0, 0.2, 0.4] TTrust[0.2, 0.4, 0.6,0.8] HTHigh Trust[0.6, 0.8, 1] UUnknown[0,0,0,0]

15. Algorithm Realization(cont.) Fuzzy Inference ◦ Inference rule : IF …THEN rule For example: IF temperature is very cold THEN turn off fan IF temperature is very hot THEN speed up fan 10/1/201515

16. Algorithm Realization(cont.) 10/1/201516 Start Set node-nearest- neighbors Retrieve node's evaluation Calculate the correlation coefficient Calculate similarity based on fuzzy reference Compute the trust recommendation End K

17. Performance Evaluation Evaluation Metrics ◦ Mean Absolute Error (MAE):  Tr i value of trust recommendation  Rr i value of real evaluation ◦ Average Packet Drop Ratio (APDR): 10/1/201517

18. Performance Evaluation(cont.) Evaluation Setup 10/1/201518 ParameterValue MAC802.11/b Area Speed[5,20] Radio range250 PlacementUniform MovementRandom waypoint ApplicationCBR Sending capacity2Mbps Packet size64B Simulation time900s

19. Performance Evaluation(cont.) Mean Absolute Error (MAE) 10/1/201519 NN SM CosineCorrelationAdjusted cosine 51.3321.3351.283 101.3131.3221.302 151.2861.2801.278 201.3021.3001.279 251.2881.3021.288 301.2941.2951.293 351.3311.3321.300 401.2791.2991.279 451.3361.2991.289 501.2911.3331.290

20. Performance Evaluation(cont.) 10/1/201520

21. Performance Evaluation(cont.) Average Packet Drop Ratio(APDR) 10/1/201521

22. Conclusion and Future Work A fuzzy trust recommendation based on collaborative filtering for MANETs. Combining local trust and trust recommendation information based on collaborative filtering to allow nodes to represent and reason with uncertainty and imprecise information regarding other nodes' trust. Some attack models will be done in the paper in the future. 10/1/201522

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24. Questions ? 10/1/201524