Fuzzy Trust Recommendation Based on Collaborative Filtering for Mobile Ad-hoc Networks

1. Fuzzy Trust Recommendation Based on Collaborative Filtering for Mobile Ad-hoc Networks Junhai Luo1,2, Xue Liu1 , Yi Zhang 3 ,Danxia Ye2 ,Zhong Xu1 1McGill University 2University of Electronic Science and Technology of China 3University of California September 2008

2. Outline • Motivations • Related Work • Architecture • Algorithm Realization • Performance Evaluation • Conclusion and Future Work

3. Motivations • MANETs characteristics: • Cooperative • Autonomous • Self-organized D j i S

4. PDA PDA Laptop computer Laptop computer Pen computer Motivations(cont.) • Low power • Multi-hop • Vulnerable to various attacks

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

6. Motivations(cont.) • Methods • Collaborative filtering • Fuzzy logic

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.

8. Architecture j1 i j2 m … K jK How? Ri Rj Rjk,m Ri,m

9. AlgorithmRealization • Local trust Value • Collaborative filtering • Fuzzy trust recommendation

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

11. Algorithm Realization(cont.) • Collaborative Filtering • Similarity Functions • Cosine-Based Similarity • Correlation-Based Similarity • Adjusted Cosine Similarity

12. Algorithm Realization(cont.) • FuzzyMethod • Fuzzy Membership Function • Fuzzy Levels • Fuzzy Inference

13. Algorithm Realization(cont.) • Fuzzy Membership Function • Trapezoid Membership Function (TMF) Degree 1 a2 a1 a4 a3 0 TrustLevels

14. Algorithm Realization(cont.) • Fuzzy Levels

15. Algorithm Realization(cont.) • Fuzzy Inference • Inference rule : IF …THEN rule Forexample: IF temperature is very cold THEN turn off fan IF temperature is very hot THEN speed up fan

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

17. Performance Evaluation • Evaluation Metrics • Mean Absolute Error (MAE): • Tri value of trust recommendation • Rri value of real evaluation • Average Packet Drop Ratio (APDR):

18. Performance Evaluation(cont.) • Evaluation Setup

19. Performance Evaluation(cont.) • Mean Absolute Error (MAE)

20. Performance Evaluation(cont.)

21. Performance Evaluation(cont.) • Average Packet Drop Ratio(APDR)

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.

23. ThankYou

24. Questions ?