Mobility pattern based misbehavior detection in vehicular adhoc networks to enhance safety

Vehicular Ad-hoc Networks (VANETs) can make roads safer, cleaner, and smarter. It can offer a wide range of services, which can be safety and non-safety related. False or bogus information is a real threat in VANET's safety applications, security and privacy. Vehicles or drivers may react to false information and cause serious problems. In VANETs Drivers' behavioral tendencies can be reflected in the mobility patterns of the vehicles. Monitoring mobility patterns of the vehicles within their transmission range, helps them in earlier detection of the correctness of the received messages. Detection of false messages is not enough to enhance the security and safety. Misbehaving vehicles need to be detected and penalized, so that they can not misbehave in the future. Existing misbehavior detection schemes have not adequately addressed this issue in the highway. In this paper we present a misbehavior detection scheme (MDS) and framework based on the mobility patterns analysis of the vehicles in the vicinity of concerned vehicles. The proposed MDS is a hybrid mechanism of both Data-Centric and Entity-Centric to cover wide range of misbehaviors. Simulation results demonstrate the potential of the proposed MDS and framework especially in highway safety applications.