Advancing Intrusion Detection in V2X Networks: A Comprehensive Survey on Machine Learning, Federated Learning, and Edge AI for V2X Security

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 are often overlooked in previous surveys. We also explore deep learning-based IDS, providing a comparative evaluation of simulated versus real-world performance. Additionally, we present an in-depth discussion on post-quantum cryptographic techniques and blockchain integration for enhancing security in Federated Learning-based IDS. This survey bridges the gap between theoretical IDS models and real-world V2X deployment, addressing key constraints such as energy efficiency, communication overhead, and scalability in resource-constrained vehicular networks. By studying the state-of-the-art methodologies, identifying critical research gaps, and proposing practical advancements, this paper serves as a definitive resource for researchers,and industry professionals, guiding the development of robust, adaptive, and privacy-preserving IDS solutions for next-generation autonomous and connected vehicles (CAVs).