# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 4 Generative AI-Based IDS for IoV  
As connected vehicles become increasingly vulnerable to cyber threats, researchers have turned to Generative AI-based Intrusion Detection Systems to enhance security in vehicular networks. Numerous studies have explored a variety of techniques to detect and mitigate attacks, leveraging models such as GANs, Transformers, VAEs, GDMs, ARMs, and others to improve detection accuracy and adaptability. Table 7 presents a comparison of various Generative AI-based IDS approaches for cybersecurity in connected vehicles, highlighting the core techniques employed in each. In particular, GANs have proven effective and suitable for detecting both known and unknown threats in real-time vehicular environments. Several works have investigated this direction, including [PERSON] et al. [67], who introduced a BiGAN-based IDS that captures latent features from network traffic, allowing the system to detect both known and unknown attacks in IoV systems. The BiGAN architecture enhances anomaly detection through bidirectional encoding and decoding processes. Using the CICIDS2017 dataset, this model demonstrates robust intrusion detection performance, but it demands high computational resources. In contrast, [PERSON] et al. [68] integrated federated learning with a multi-level distributed GAN model, offering a solution tailored for resource-constrained environments in IoV. This decentralized learning approach reduces the burden on central servers and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Generative Model** & **Core Mechanism** & **Key Strengths** \\ \hline \multirow{4}{*}{GANs} & Involes adversarial training between two networks: a generator (produces synthetic data) and a discriminator (evaluates authenticity). & –High-quality data synthesis —Learns complex data distributions \\ \hline \multirow{3}{*}{Transformers} & Utilizes self-attention mechanisms to process all elements of an input sequence in parallel, capturing long-range dependencies efficiently. & –Handles long-range dependencies —Scalable and parallelizable —Pretrained on large data \\ \hline \multirow{3}{*}{VAEs} & Learns a probabilistic latent representation by encoding and decoding input data, optimizing a balance between reconstruction and generalization. & –Probabilistic reasoning —Low reconstruction error alerts —Good latent representations \\ \hline \multirow{3}{*}{GDMs} & Learns to reverse a structured noise process by progressively denoising the corrupted input, based on diffusion and generative modeling. & –High sample quality —Robust to noise —Good uncertainty estimation \\ \hline \multirow{3}{*}{ARMs} & Predicts future values in a sequence based on past observations; can be extended with regime-switching mechanisms. & —Effective in time series —Adapts to changes over time —Interpretable \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative summary of Generative AI models. improves scalability and efficiency. However, it introduces synchronization overhead and communication complexity, especially in dynamic vehicular environments. Moreover, [PERSON] et al. [69] proposed a collaborative intrusion detection framework utilizing a multi-discriminator GAN. This enables multiple distributed SDN controllers to jointly train an intrusion detection model for an entire VANET without directly exchanging sub-network flows. The framework was validated using KDD99 and NSL-KDD datasets on an emulated cloud server with three distributed Software-defined Networking (SDN) controllers. Although the authors rigorously validated their system in both IID and non-IID contexts with mathematical proofs, no numerical results for evaluated metrics were presented. Furthermore, Ref. [70] proposed an anomaly detection algorithm using GANs to address the imbalance between normal and attack traffic in vehicular networks. The algorithm, trained solely on normal traffic data, employs the Gini index for dimensionality reduction and normalization functions to enhance training speed. Moreover, in Ref.