The Evolving Security of the Internet of Vehicles: A Survey from Classical Machine Learning to Large Language Models, Adversarial Robustness, and Explainable AI

Abstract

The Internet of Vehicles (IoV) connects vehicles, roadside infrastructure, and cloud or edge platforms to support safer and more efficient transportation. This connectivity also exposes vehicular networks to message spoofing, denial-of-service, and man-in-the-middle attacks, and to threats that target the machine-learning defenses themselves, such as model poisoning, gradient inversion, and adversarial examples. Machine learning and deep learning are now the dominant defensive tools, and the resulting literature is large and fragmented. This survey reviews that literature using a documented database search and stated inclusion criteria. The retained studies are organized into three categories, each defined by the dominant security-design problem it addresses. The categories cover classical detection and prevention, decentralized security based on federated learning and blockchain, and the assurance directions of Large Language Model (LLM)-driven detection, adversarial robustness, and explainable artificial intelligence. Rather than pooling reported scores, the survey records the evaluation setting of each study. This shows that de-duplication, class balancing, and metric aggregation account for much of the apparent variation in reported performance. A study-level assessment finds that no reviewed study demonstrates more than one of the three assurance capabilities, and no detector evaluated on vehicular traffic has been tested against adversarial perturbation. The survey concludes with a cross-category comparison, an EU AI Act alignment assessment, and recommendations for future research.