A Hybrid Deep Sensor Anomaly Detection for Autonomous Vehicles in 6G-V2X Environment

Autonomous Vehicles (AVs) exchange real-time and seamless data between other AVs and the network, thus revolutionizing the Intelligent Transportation System (ITS). Automated transportation brings numerous benefits to human beings. However, the concerns such as safety, security, and privacy keep rising. In navigation and trajectory planning, the AVs require exchanging sensory information from their own and other AVs. In such cases, when a malicious AV or faulty sensor-equipped AV comes into connectivity, it can have disruptive consequences. This paper proposes a Hybrid Deep Anomaly Detection (HDAD) approach for effective anomaly detection and cyber-attack mitigation in AVs. The Multi-Agent Reinforcement Learning (MARL) algorithm in HDAD approach acts over the 6G network to combat new-age cyber-attacks and provide a swift and accurate anomaly detection mechanism. In conjunction with Maximum Entropy Inverse Reinforcement Learning (MaxEntIRL), the HDAD approach identifies and isolates malicious AVs. It is envisioned that the obtained results prove the effectiveness of HDAD and have an 8.2% higher accuracy rate than the existing systems.

Automated summary

Autonomous Vehicles (AVs) revolutionize the Intelligent Transportation System (ITS) by exchanging real-time and seamless data between each other and the network. While offering numerous benefits, concerns about safety, security, and privacy are increasing. This paper proposes a Hybrid Deep Anomaly Detection (HDAD) approach that uses Multi-Agent Reinforcement Learning (MARL) and Maximum Entropy Inverse Reinforcement Learning (MaxEntIRL) to effectively detect anomalies and mitigate cyber-attacks in AVs. The results show that HDAD has an 8.2% higher accuracy rate compared to existing systems.