Robust Misbehavior Detection Scheme for Vehicular Network

Vehicular Ad Hoc Network VANET is emerged to improve future Intelligence Transportation Systems (ITS) and improve road safety and traffic efficiency as well as provide passenger comfort. Vehicles in VANET share their mobility status to improve application performance and provide network agility. However, misbehaving vehicles that send false mobility information can disrupt the fundamental operation of potential V ANET applications. Many solutions have been proposed to detect misbehavior data. However, most of these solutions are vulnerable to consistency-based attackers. Attackers can create false but consistent information and share it with neighboring vehicles causing serious driving situations. This study aims to design and develop a robust misbehavior detection scheme for VANET. A directional antenna and the received signal characteristics have been incorporated in the Kalman Filter state to validate the consistency of the received cooperative awareness messages. The received signal strength indicator (RSSI) and the angle of arrival (AoA) were used as trusted features that a vehicle can use to validate the correctness of the cooperative awareness messages received from neighboring vehicles. The innovation errors of the Kalman filter are used to determine the false information. That is if the message contains diverge much from its signal properties the message is considered misbehavior. The results of this study show the effectiveness of the proposed scheme for narrowing down the attack space in the future cooperative ITS.