Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks

Purpose: The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands, and distributed communication protocols that expose vast attack surfaces around both individual and network-wide safety. Existing security schemes are unable to provide sub-10 ms (milliseconds) anomaly detection and distributed coordination of large-scale networks of vehicles within an acceptable safety/privacy framework. Method: This paper introduces a three-tier hybrid security architecture HA VEN (Hierarchical Autonomous Vehicle Enhanced Network), which decouples real-time local threat detection and distributed coordination operations. It incorporates a light ensemble anomaly detection model on the edge (first layer), Byzantine-fault-tolerant federated learning to aggregate threat intelligence at a regional scale (middle layer), and selected blockchain mechanisms (top layer) to ensure critical security coordination. Result: Extensive experimentation is done on a real-world autonomous driving dataset. Large-scale simulations with the number of vehicles ranging between 100 and 1000 and different attack types, such as sensor spoofing, jamming, and adversarial model poisoning, are conducted to test the scalability and resiliency of HA VEN. Conclusion: The proposed framework overcomes the important tradeoff between real-time safety obligation and distributed security coordination with novel three-tiered processing. The scalable architecture of HAVEN is shown to provide great improvement in detection accuracy as well as network resilience over other methods. Introduction The unprecedented rise of autonomous vehicles (A V) has altered the transport industry by providing unparalleled connectivity, intelligence, and an accessible transportation medium to people. These vehicles process mul-timodal sensor data collected from LiDAR, cameras, radar, and GPS/IMU sensors networked on a CAN (Controller Area Network) bus, generating terabytes of data in a day that require real-time analysis for safe operation [1, 2, 3]. However, the automotive systems are marred by state-of-the-art security threats characterized by safety-critical domains. Further, the distributed nature of vehicular networks poses significantly higher computational complexity and inherent difficulties while developing efficient cyberse-curity frameworks for detecting wide-array of threats in real-time.