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

TL;DR

This paper presents HAVEN, a scalable three-tier hybrid security framework for autonomous vehicle networks that achieves sub-10 ms anomaly detection with high accuracy, resilience, and privacy, addressing real-time security challenges in large-scale autonomous systems.

Contribution

The paper introduces HAVEN, a novel hierarchical architecture combining edge anomaly detection, federated learning, and blockchain for scalable, real-time security in autonomous vehicle networks.

Findings

Sub-10 ms detection latency achieved.

94% detection accuracy and 92% F1-score.

Validated Byzantine fault tolerance with 20% compromised nodes.

Abstract

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. This paper introduces a three-tier hybrid security architecture HAVEN (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. 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 HAVEN. Experimental findings show sub-10 ms detection latency with an accuracy of 94% and F1-score of 92% across multimodal sensor data, Byzantine fault tolerance validated with 20\% compromised nodes, and a reduced blockchain storage overhead, guaranteeing sufficient differential privacy. The proposed framework overcomes the important trade-off 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.