Detecting Vehicle Anomaly by Sensor Consistency: An Edge Computing Based Mechanism

Autonomous vehicles are expected to be a disruptive technology that has the potential to revolutionize the human mobility. However, the recent research progress on intra-vehicle network (e.g., the revealing of a series of security vulnerabilities of CAN design) has demonstrated that the security issue still represents one of the major challenges of future self-driving cars. In this study, we propose a novel edge based anomaly detection system, coined VeAnDe, which exploits edge based sensor data fusion to identify the anomaly events. VeAnDe analyzes pair-wise correlations between different intra-vehicle sensors, and utilizes these correlations to examine whether an anomaly has occurred within the vehicle. More specifically, the pair-wise correlations are organized as ring architecture to reduce the computation overhead. Furthermore, the major components of VeAnDe are embedded in edge computing devices, which enables VeAnDe to be more efficient and privacy-preserving. We evaluate the performance of VeAnDe under different scenarios, and our experimental results demonstrate its feasibility and efficiency.