Vehicle driving pattern based sybil attack detection

In recent years, vehicular networks have been drawing special attention because of its significant potential role in future smart city regarding traffic efficiency improvement and road safety. Safety's crucial status in vehicular networks is determined by its direct impact on people's lives. Several security services based on cryptography, PKI and pseudonymous have been standardized in the past few years by IEEE and ETSI. However, vehicular networks are still vulnerable to critical attacks and the Sybil attack is one of them. This paper proposes a Sybil attack detection method based on vehicle driving pattern in urban scenario. In this method, Driving Pattern Matrices (DPMs) are constructed for each vehicle based on the beaconing messages they communicate. Then, a minimum distance classifier is used to evaluate their driving pattern and detect the unusual pattern. The simulation results show that our detection method can reach a high detection rate with a low error rate.