{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T13:56:47Z","timestamp":1790776607913,"version":"4.1.0"},"reference-count":189,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s26196211","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T13:01:19Z","timestamp":1790773279000},"page":"6211","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Driven Intrusion Detection for Vehicular Networks: A Comprehensive Survey of Techniques, Datasets, Deployment Architectures, and Future Directions"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6206-3934","authenticated-orcid":false,"given":"Syed Rizwan","family":"Hassan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sadia","family":"Din","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6991-1481","authenticated-orcid":false,"given":"Muhammad Ismail","family":"Mohmand","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Faculty of Engineering and Natural Sciences, Istanbul Atlas University, Istanbul 34408, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.58496\/BJAI\/2025\/008","article-title":"Artificial intelligence in malware and network intrusion detection: A comprehensive survey of techniques, datasets, challenges, and future directions","volume":"2025","author":"Moamin","year":"2025","journal-title":"Babylon. 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