Causal Inference-Based Network Anomaly Detection for Internet of Vehicles

Abstract

5G-V2X technology and connected and autonomous vehicles are deeply in-tegrated. The Internet of Vehicles (IoV) has become the core support of an intelligent transportation system. Its heterogeneous architecture and high-dynamic characteristics bring serious cybersecurity risks. Traditional anoma-ly detection methods rely on correlation reasoning. They have high false pos-itive rates. They are not easy to interpret. They can’t adapt to combined at-tacks. They also lack real-time performance. This paper puts forward a causal inference-based anomaly detection algorithm (IoV-NDCML). It is specially designed for the IoV scenario. The algorithm reconstructs a multi-dimensional causal interpretable feature set. It designs a dynamic causal in-tervention screening method. The method integrates scenario weights. It builds a lightweight hierarchical SCM model. It improves the counterfactual diagnosis method. The algorithm realizes accurate anomaly detection. It also achieves causal attribution.Experiments show that the proposed algorithm achieves 99.2% accuracy in single-attack scenarios and 97.5% accuracy in combined-attack scenarios, with an end-to-end latency below 50 ms on edge devices. Its comprehensive performance outperforms comparative algo-rithms, providing technical support for security defense in the Internet of Vehicles.