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Intrusion Detection in Autonomous Vehicular Networks: A Trust Assessment and Q-learning Approach

The ever-growing development of autonomous driving vehicles (ADVs) attracts a lot of attention of the intelligent industry. The security issues of the autonomous vehicular networks become essential since the automated components onboard are easily attacked which may leads ADVs malfunction. However, few of the intrusion detection systems (IDSs) used for mitigating above issues consider the intrusion detection in autonomous vehicular networks (AVNs). Therefore, in this paper, we propose a novel intrusion detection scheme for AVNs. Firstly, we establish the trust assessment model for ADVs including direct assessment and indirect assessment. Then, we present the intrusion detection model by confidence assessment to intrusion reports. Next, a Q-learning based incentive model is given to intelligently stimulate ADVs to participate the intrusion reporting. Finally, the simulation results show the efficiency of our proposal compared with conventional schemes in detection rate.