Special Issue Article
Sybil Attack Detection in Urban Vehicular Networks
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Mobility is often a problem for providing security services in adhoc networks. in this paper, we show that mobility can be used to enhance security. Specifically, use show that nodes that passively monitor traffic in the network identities simultaneously. We show through simulation that this detection can be done by a single node or that multiple trusted nodes can join to improve the accuracy of detection. In urban vehicular networks, where privacy, especially the location privacy of anonymous vehicles is highly concerned, anonymous verification of vehicles is indispensable. Consequently, an attacker who succeeds in forging multiple hostile identifies can easily lunch a Sybil attack, gaining a disproportionately large influence. In this paper, we propose a novel Sybil attack detection mechanism, foot print, using the trajectories of vehicles for identification while still preserving their location privacy. More specifically, when a vehicle approaches a road-side unit (RSU), it actively demands an authorized message from the RSU as the proof of the appearance time at this time RSU. We design a location- hidden authorized message generation scheme for two objectives: first, RSU signatures on message are signer ambiguous so that the RSU location information is concealed from the resulted authorized message; second ,two authorized message signed by the same RSU within the same given period of time are recognizable so that they can be used for identification . With the temporal limitation on the likability of two authorized messages, authorized messages used for long-term identification are prohibited. With this scheme, vehicles can generate a locationhidden trajectory for location- privacy- preserved identification by collecting a consecutive series of authorized messages. Foot print can recognize and therefore dismiss “communities” of Sybil trajectories. Rigorous security analysis and extensive trace-driven simulations demonstrate the efficacy of foot print.