Detecting Sybil attacks in VANETs

Sybil attacks have been regarded as a serious security threat to Ad hoc Networks and Sensor Networks. They may also impair the potential applications in Vehicular Ad hoc Networks (VANETs) by creating an illusion of traffic congestion. In this paper, we make various attempts to explore the feasibility of detecting Sybil attacks by analyzing signal strength distribution. First, we propose a cooperative method to verify the positions of potential Sybil nodes. We use a Random Sample Consensus (RANSAC)-based algorithm to make this cooperative method more robust against outlier data fabricated by Sybil nodes. However, several inherent drawbacks of this cooperative method prompt us to explore additional approaches. We introduce a statistical method and design a system which is able to verify where a vehicle comes from. The system is termed the Presence Evidence System (PES). With PES, we are able to enhance the detection accuracy using statistical analysis over an observation period. Finally, based on realistic US maps and traffic models, we conducted simulations to evaluate the feasibility and efficiency of our methods. Our scheme proves to be an economical approach to suppressing Sybil attacks without extra support from specific positioning hardware.

► Proposed a cooperative method to verify the positions of potential Sybil nodes.
► Studied a Random Sample Consensus-based algorithm to rule out outlier data.
► Proposed a statistical method to overcome the drawbacks of the cooperative method.
► Simulations evaluated the performance based on realistic US maps and traffic models.