Privacy-Aware Anomaly Detection and Notification Enhancement for VANET Based on Collaborative Intrusion Detection System

Collaborative Intrusion Detection System (CIDS) is an essential technology that enables vehicular ad hoc networks (VANET) to protect against malicious intrusions. CIDS, however, is unable to prevent accidents if an anomalous vehicle is detected. Detecting anomalies and notifying vehicles in the VANET rapidly is thus essential, considering technical challenges such as communication efficiency, vehicle velocity and privacy. In this paper, we propose a novel two-layer privacy-aware trust evaluation CIDS framework, termed 2PT-CIDS, tailored to VANET. In 2PT-CIDS, vehicles and roadside units (RSUs) cooperate efficiently to enhance anomalous vehicle detection and notification. Considering its potential privacy leakage, we then present two types of game-theoretic information incentive mechanisms. In the case of traffic congestion, the privacy-aware incentive mechanism is presented based on the Stackelberg game. A Barycentric Lagrange interpolation (BLI) based algorithm is then proposed to speedy achieve the Nash equilibrium (NE). In the case of traffic smooth, the varying high velocities of vehicles are involved and a noncooperative game-based mechanism is proposed. The optimal NE decision selection is reconstructed as a Markov decision process (MDP) and the NE point is obtained via the designed novel reward-shaping double duelling deep Q network (D3QN) learning algorithm. Simulation results highlight the superiority of 2PT-CIDS over existing CIDS and potential application algorithms for VANET, effectively enhancing anomaly detection and notification considering communication cost and vehicle privacy.