A trust based Information sharing model (TRUISM) in MANET in the presence of uncertainty

In the absence of centralized trusted authorities (CTA), security is one of the foremost concern in Mobile Ad-hoc Networks (MANET) as the network is open to attacks and unreliability in the presence of malicious nodes (devices). With increasing demand of interactions among nodes, trust based information sharing needs more stringent rules to ensure security in this pervasive computing scenario. In this paper, we present a novel multi-hop recommendation based trust management scheme (TRUISM). We adapt famous Dempster-Shafer theory that can efficiently combine recommendations from multiple devices in the presence of unreliable and malicious recommendations. A novel recommendation-routing protocol named `buffering on-the-fly' has been introduced to reduce the number of recommendation traffic by storing trust values in intermediate nodes. TRUISM also provides a flexible behavioral model for trust computation where a node can prioritize recommendations based on its requirements. Evaluation result shows that our model not only performs well in the presence of contradictory recommendations but also ensures a faster and scalable trust based information sharing by reducing the overall packet flow in the system.

[1]  Celeste Campo,et al.  PTM: A Pervasive Trust Management Model for Dynamic Open Environments ⁄ , 2003 .

[2]  G. Pulla,et al.  A SURVEY ON TRUST MANAGEMENT FOR MOBILE AD HOC NETWORKS , 2010 .

[3]  Indrajit Ray,et al.  TrustBAC: integrating trust relationships into the RBAC model for access control in open systems , 2006, SACMAT '06.

[4]  Ruidong Li,et al.  Future trust management framework for mobile ad hoc networks , 2008, IEEE Communications Magazine.

[5]  Jordi Sabater-Mir,et al.  REGRET: reputation in gregarious societies , 2001, AGENTS '01.

[6]  Audun Jøsang,et al.  Semantic Constraints for Trust Transitivity , 2005, APCCM.

[7]  Sheikh Iqbal Ahamed,et al.  An Omnipresent Formal Trust Model (FTM) for Pervasive Computing Environment , 2007, 31st Annual International Computer Software and Applications Conference (COMPSAC 2007).

[8]  Nicholas R. Jennings,et al.  Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model , 2005, AAMAS '05.

[9]  Frank Stajano,et al.  The Resurrecting Duckling: security issues for ubiquitous computing , 2002, S&P 2002.

[10]  Vijay Varadharajan,et al.  Subjective logic based trust model for mobile ad hoc networks , 2008, SecureComm.

[11]  Audun Jøsang,et al.  Trust network analysis with subjective logic , 2006, ACSC.

[12]  Jean-Yves Le Boudec,et al.  The Effect of Rumor Spreading in Reputation Systems for Mobile Ad-hoc Networks , 2003 .

[13]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[14]  John S. Baras,et al.  On trust models and trust evaluation metrics for ad hoc networks , 2006, IEEE Journal on Selected Areas in Communications.

[15]  Stephen Hailes,et al.  Supporting trust in virtual communities , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[16]  Otto Carlos Muniz Bandeira Duarte,et al.  Trust management in mobile ad hoc networks using a scalable maturity-based model , 2010, IEEE Transactions on Network and Service Management.

[17]  Qi He,et al.  SORI: a secure and objective reputation-based incentive scheme for ad-hoc networks , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[18]  Mary Baker,et al.  Mitigating routing misbehavior in mobile ad hoc networks , 2000, MobiCom '00.

[19]  Joan Feigenbaum,et al.  Decentralized trust management , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[20]  Rino Falcone,et al.  Trust dynamics: how trust is influenced by direct experiences and by trust itself , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..