Advancing Intrusion Detection in V2X Networks: A Comprehensive Survey on Machine Learning, Federated Learning, and Edge AI for V2X Security
Abstract
The security of Vehicle-to-Everything (V2X) networks is fundamental to the realization of next-generation intelligent transportation systems. However, the dynamic nature of V2X environments introduces critical challenges in ensuring robust Intrusion Detection Systems (IDS), particularly concerning false alarm rates, adversarial attacks, computational complexity, and real-world deployment constraints. Traditional centralized machine learning-based IDS suffer from high computation costs, privacy risks, bandwidth constraints, and scalability limitations, making them impractical for real-time, distributed vehicular networks. To address these gaps, this paper provides a comprehensive and structured survey of IDS methodologies in V2X security, focusing on Federated Learning (FL) and Edge AI for privacy-preserving and scalable IDS solutions. Unlike prior works, we systematically analyze and benchmark intrusion detection datasets, highlighting limitations in detecting zero-day attacks and exploring the need for hybrid datasets that integrate real-world vehicular data with adversarial attack scenarios. Furthermore, we investigate the adversarial robustness of ML-based IDS, analyzing AI-based evasion techniques, data poisoning threats, and misbehavior detection challenges. A key novelty of this work lies in the detailed examination of computational complexities in IDS deployment, including sensor fusion methods, noise reduction techniques, and false alarm mitigation strategies, which…
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