AI-Driven Intrusion Detection for Vehicular Networks: A Comprehensive Survey of Techniques, Datasets, Deployment Architectures, and Future Directions

AI-Driven Intrusion Detection for Vehicular Networks: A Comprehensive Survey of Techniques, Datasets, Deployment Architectures, and Future Directions. Intelligent transportation systems (ITSs), Vehicle-to-Everything (V2X) communication and autonomous driving technologies have brought about significant changes in the modern vehicular network. At the same time, the cyber-attack surface has grown, leading to new and existing advanced security threats for Vehicular Ad hoc Networks (VANETs) and the Internet of Vehicles (IoV). Traditional IDSs that employ static rule-based techniques are often insufficient in the context of the vehicular environment, characterized by dynamic, highly mobile systems with stringent low-latency requirements. Next generation IDS development has shifted to artificial intelligence (AI), which offers adaptive, scalable and data-driven detection capabilities. In this survey, the authors present a comprehensive overview of the state-of-the-art of AI-driven vehicle IDS in terms of machine learning (ML), deep learning (DL), federated learning (FL), reinforcement learning (RL), transformer-based techniques, and graph neural network (GNN) designs. We present a multidimensional taxonomy of vehicular attacks, an analysis of existing datasets and their weaknesses, an evaluation of the performance of AI techniques and a study of potential deployment architectures such as edge, fog and cloud. Furthermore, some of the most important open problems are identified, including data scarcity, adversarial robustness, real-time constraints, data utility-privacy conflicts, and standardization gaps. A research roadmap is provided, including 6G connectivity, blockchain-based IDS, explainable AI (XAI), Digital Twin technologies and post-quantum cryptography. This survey provides a common reference for the designers and developers of secure, efficient and interoperable VIDSs.