Performance analysis of the CONFIDANT protocol
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
Core: a collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks
Proceedings of the IFIP TC6/TC11 Sixth Joint Working Conference on Communications and Multimedia Security: Advanced Communications and Multimedia Security
A Reputation-based Mechanism for Isolating Selfish Nodes in Ad Hoc Networks
MOBIQUITOUS '05 Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
Dempster-Shafer Theory for Intrusion Detection in Ad Hoc Networks
IEEE Internet Computing
Robust cooperative trust establishment for MANETs
Proceedings of the fourth ACM workshop on Security of ad hoc and sensor networks
LARS: a locally aware reputation system for mobile ad hoc networks
Proceedings of the 44th annual Southeast regional conference
Outlier Detection in Ad Hoc Networks Using Dempster-Shafer Theory
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Advanced detection of selfish or malicious nodes in ad hoc networks
ESAS'04 Proceedings of the First European conference on Security in Ad-hoc and Sensor Networks
Information theoretic framework of trust modeling and evaluation for ad hoc networks
IEEE Journal on Selected Areas in Communications
Management and applications of trust in Wireless Sensor Networks: A survey
Journal of Computer and System Sciences
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Selfishness detection is becoming a hot issue in mobile ad hoc networks and wireless sensor networks. We use Dempster-Shafer theory of evidence in a novel way to incorporate data-centric trust evaluation for detection of nodes' selfish forwarding behavior. Within the proposed D2S2T2 framework, trust is considered in regard to forwarding, as part of routing support, as well as in regard to recommendations, as part of cooperation enforcement support. D2S2T2 takes care to control the impact of third-party nodes to make the system more robust against malicious attacks, while minimizing the time taken to detect all selfish nodes in the network. Preliminary simulations shed light on the influence of recommender location and recommendation content.