# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 4 Generative AI-Based IDS for IoV
[71], the authors used GANs to identify driving anomalies, leveraging multiple modalities such as CAN bus data, physiological signals, and environmental information. While this method significantly enhances the detection of irregular driving patterns, its scalability is limited, and it cannot easily incorporate additional modalities. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**GAI Technique** & **Ref** & **Main Approach** & **Detection Target** & **Network Focus** & **Performance Summary** & **Strengths and Limitations** \\ \hline \multirow{4}{*}{[67]} & \multirow{4}{*}{[67]} & BiGAN (Encoder-Discriminator) & Known and unknown attacks & \multirow{4}{*}{V2V} & Acc: 92.15\% & \multirow{4}{*}{Robust and strong performance} \\  & & & & F1: 0.961 & & \\  & & & unknown attacks & Latency: 83.71 s & & \\  & & & & FRR: 7.3 & & \\  & & & & FNR: 0.6 & & \\ \hline \multirow{4}{*}{[68]} & Distributed & \multirow{4}{*}{Malicious traffic} & \multirow{4}{*}{V2X} & Acc: 98.92\% & \multirow{4}{*}{Scalable and decentralized} \\  & GANs + Federated & & behavior & & F1: 0.77 & \\  & Learning & & & & \\ \hline \multirow{4}{*}{[69]} & Collaborative & \multirow{4}{*}{Multi-Discriminator} & \multirow{4}{*}{Intrusion detection} & \multirow{4}{*}{V2X} & \multirow{4}{*}{—} & Collaborative framework, \\  & Multi-Discriminator & & & & validated on datasets \\  & GAN (SDN) & & & & \\ \hline \multirow{4}{*}{[70]} & GAN-based & \multirow{4}{*}{Traffic anomalies} & \multirow{4}{*}{V2X} & Acc: 94.9\% & \multirow{4}{*}{Addresses traffic imbalance} \\  & Anomaly & Detection & & & & \\ \cline{1-1}  & \multirow{4}{*}{GANs} & Multi-modal & \multirow{4}{*}{Driving anomaly detection} & \multirow{4}{*}{V2X} & \multirow{4}{*}{—} & \multirow{4}{*}{High detection accuracy} \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & \multirow{4}{*}{GANs} & \multirow{4}{*}{CAN messages} & \multirow{4}{*}{IVN} & Acc: 98\% & \multirow{4}{*}{High accuracy} \\  & & & & Latency: 0.18 s & & \\ \hline \multirow{4}{*}{[73]} & Dual- & \multirow{4}{*}{CAN-FD bus} & \multirow{4}{*}{IVN} & Acc: 99.93\% & \multirow{4}{*}{High accuracy (99.93\%)} \\  & GAN with & & & Latency: 0.15 ms & & \\ \cline{1-1}  & ID encoding & & & per message & & \\ \hline \multirow{4}{*}{[74]} & Combining CAN & \multirow{4}{*}{Tampering detection} & \multirow{4}{*}{IVN} & \multirow{4}{*}{—} & \multirow{4}{*}{High precision and accuracy} \\  & & matrix with & & & & \\ \cline{1-1}  & image-based & & & & \\ \cline{1-1}  & GANs & & & & \\ \hline \multirow{4}{*}{[75]} & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Attacks classification} & \multirow{4}{*}{IVN} & F1: 99.23 & \multirow{4}{*}{High accuracy and low} \\  & & & Latency: & & \\ \cline{1-1}  & two-stage & & 0.203 ms (multicore) & & \\ \cline{1-1}  & classification & & 0.538 ms (single-core) & & \\ \cline{1-1}  & & & & FPR: 0.36 & \\ \cline{1-1} \cline{2-2} \cline{4-6} \cline{1-1}  & & & & FNR: 0.96 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of Generative AI-based IDS for IoV. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
As we shift focus to in-vehicle networks (IVNs), [PERSON] et al. [72] proposed a GAN-based Intrusion Detection System, a model that uses GAN to detect both known and unknown attacks on in-vehicle CAN bus networks. By employing a dual-discriminator approach, GIDS identifies attacks using only normal data, achieving high accuracy in detecting known attacks and around 98% accuracy for unknown ones. However, it focused only on known CAN traffic formats and was limited by the quality of generated data. In a similar vein, [PERSON] et al. [73] focused on the CAN-FD protocol and proposed a GAN with dual discriminators to detect ID-based intrusions. By encoding message IDs into binary images and feeding them into the GAN, the system distinguishes between normal and malicious ID patterns. The model achieves remarkable accuracy (99.93%) and low latency but is limited in its application to CAN-FD ID segments. In another approach, [PERSON] et al.