# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 1 Introduction
### Existing Reviews and Our Contributions  
Intrusion Detection Systems have become a vital component in the security architecture of the IoV, given the increasing attack surface introduced by vehicle connectivity and autonomous functionalities. Several survey papers have examined IDS development in the context of IoV and internal vehicle networks, particularly highlighting the role of artificial intelligence (AI) in enhancing detection accuracy and adaptability. [PERSON] et al. [1] provide a broad overview of AI-based IDS in IoV, covering both internal vehicle networks and external communication links such as V2V and V2I. Their taxonomy spans misuse-based, anomaly-based, and hybrid models, and emphasizes the importance of anomaly detection for unknown threat mitigation. However, the review remains general in scope; lacks technical depth regarding deep learning architectures; and does not explore Generative AI-based IDS techniques, such as GANs or transformer models, which are increasingly applied in cybersecurity for detecting novel, unseen attacks. In the context of in-vehicle networks (IVNs), several surveys have explored AI-based intrusion detection approaches. [PERSON] et al. [2] provide a focused review of DL-based IDS models for CAN-based systems, proposing a taxonomy based on detection strategies, attack types, datasets, and AI techniques. Their work introduces key emerging methods such as federated learning and reinforcement learning, though it lacks coverage of transformer-based or Generative AI methods. Complementing this, [PERSON] et al. [3] present one of the most detailed and recent surveys dedicated exclusively to AI-based IDS for IVNs (2016-2022). They introduce a novel taxonomy that categorizes models based on the features used and the type of AI technique (ML, DL, hybrid), and include various attack types. Notably, this survey also reviews the security of AI models, potential threats against them, and practical deployment steps. While this work does mention GANs and even a few uses of Transformers for sequence modeling, the coverage is still sparse and exploratory rather than systematic. Generative AI is not treated as a distinct category in their taxonomy, and the latest LLM-based or fine-tuned transformer models are not considered. A comprehensive taxonomy in Table 1 is introduced below, organizing existing AI-based IDS surveys in vehicular networks. Despite the growing interest in AI-based IDS for IVNs and broader IoV systems, a systematic investigation into the role of Generative AI remains missing from the literature. Existing reviews either focus narrowly on traditional ML/DL techniques or treat generative methods--such as GANs or Transformers--as isolated case studies without integrating them into a broader security framework. No prior work provides a dedicated taxonomy or structured analysis of Generative AI-based IDS across both internal (IVNs) and external (V2X) vehicular networks. Given the rise of data-driven attacks and the complexity of vehicular communication, this gap significantly limits the understanding of generative models' full potential in securing next-generation transportation systems. To bridge this gap, this survey provides the first in-depth analysis of Generative AI-based Intrusion Detection Systems for the Internet of Vehicles. Unlike prior reviews that overlook or briefly mention generative models, this work focuses specifically on the application of GANs and Transformer-based architectures in vehicular security. It offers a comprehensive taxonomy of generative IDS approaches, categorizing them based on the type of generative model, deployment context (in-vehicle vs. external networks), and targeted attack types. In addition, the survey critically analyzes how these models handle core challenges, such as real-time detection, data scarcity, and generalization to unseen attacks, and deployment under resource-constrained environments typical of vehicular systems.