Detecting false messages in vehicular ad hoc networks based on a traffic flow model

In vehicular ad hoc networks, inside attackers can launch a false information attack by injecting false emergency messages to report bogus events such as traffic accidents. In this article, a false message detection scheme is proposed and evaluated. First, traffic flow theory is employed to analyze vehicular behavior under a traffic accident scenario. It shows that a “bottleneck†phenomenon is triggered because the road capacity is reduced by blocked lanes at an accident site. The traffic parameters, such as vehicular density, exhibit a distinct statistical property compared to an accident-free scenario. Based on this, a false message detection algorithm is proposed in which the traveling vehicles are exploited as witnesses to collect traffic parameters, and their observation data are used as evidence to feed a traffic flow model. A Bayesian theorem–based method is used to calculate the likelihood for each traffic scenarios, and the actual traffic condition is estimated to determine whether the reported accident has actually occurred. Finally, the performance of the proposed scheme was verified through simulations in a realistic traffic scenario. It was shown that a higher detection accuracy could be obtained compared to previously proposed approach.