Detection and localization of sybil nodes in VANETs
DIWANS '06 Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks
Certain trust: a trust model for users and agents
Proceedings of the 2007 ACM symposium on Applied computing
Enhancing the Security of Local DangerWarnings in VANETs - A Simulative Analysis of Voting Schemes
ARES '07 Proceedings of the The Second International Conference on Availability, Reliability and Security
Managing Reputation over MANETs
IAS '08 Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security
TEREC: Trust Evaluation and Reputation Exchange for Cooperative Intrusion Detection in MANETs
CNSR '09 Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference
A Robust Detection of the Sybil Attack in Urban VANETs
ICDCSW '09 Proceedings of the 2009 29th IEEE International Conference on Distributed Computing Systems Workshops
EURASIP Journal on Wireless Communications and Networking - Special issue on wireless network security
Distributed misbehavior detection in VANETs
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
A Survey on Trust Management for VANETs
AINA '11 Proceedings of the 2011 IEEE International Conference on Advanced Information Networking and Applications
TRIP, a trust and reputation infrastructure-based proposal for vehicular ad hoc networks
Journal of Network and Computer Applications
POSITION VERIFICATION APPROACHES FOR VEHICULAR AD HOC NETWORKS
IEEE Wireless Communications
Eviction of Misbehaving and Faulty Nodes in Vehicular Networks
IEEE Journal on Selected Areas in Communications
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Trustworthy communication in vehicular ad-hoc networks is essential to provide functional and reliable traffic safety and efficiency applications. A Sybil attacker that is simulating "ghost vehicles" on the road, by sending messages with faked position statements, must be detected and excluded permanently from the network. Based on misbehavior detection systems, running on vehicles and roadside units, a central evaluation scheme is proposed that aims to identify and exclude attackers from the network. The proposed algorithms of the central scheme are using trust and reputation information provided in misbehavior reports in order to guarantee long-term functionality of the network. A main aspect, the scalability, is given as misbehavior reports are created only if an incident is detected in the VANET. Therefore, the load of the proposed central system is not related to the total number of network nodes. A simulation study is conducted to show the effective and reliable detection of attacker nodes, assuming a majority of benign misbehavior reporters. Extensive simulations show that a few benign nodes (at least three witnesses) are enough to significantly decrease the fake node reputation and thus identify the cause of misbehavior. In case of colluding attackers, simulations show that if 37% of neighbor nodes cooperate, then an attack could be obfuscated.