## 4 Findings and Discussion  
### ITS/IoV Intrusion Detection Systems (IDS)  
An intrusion is any unauthorized activity that disrupts or damages a network. This means that any incursion that violates the integrity, confidentiality, or availability of the ITS is an intrusion. The objective is to detect any abnormal activity in an IoV network traffic that a standard firewall cannot detect. It is critical to attain high levels of security against actions that jeopardize system availability, integrity, or confidentiality. IDSs can be classified based on the detection or deployment approach, and Figure 5 depicts the classification of an IDS. From the standpoint of the deployment-based IDS approach, the classification of IDS can be either host or network based. The host-based system monitors all activity on the single host and examines security vulnerabilities and intrusions. The major limitation with this system is that it must be on all hosts that need intrusion protection, resulting in increased computational complexity for each node and eventually degrading IDS performance [112]. On the other hand, a network-based system is to mitigate intrusions on all devices and the entire network. The network-based system constantly monitors network traffic and investigates for intrusions. On the other hand, the detection-based IDS approach can be either signature or anomaly-based. The signature-based system (also known as knowledge-based IDS) defines a signature for attack behaviors derived from footprints left by each intrusion. These signatures are in the signature database, and data patterns are matched with these signatures to detect attacks. One advantage of this system is the high detection rate due to the database's availability of attack signatures residents. However, the approach is unsuitable in real-time due to the lack of signature patterns; therefore, it is incapable of detecting new attacks. However, the approach considered in this study is the network and anomaly-based IDS, which focuses on various AI approaches. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **XAI Framework** & **Specific Usage** & **Detail** & **Year** \\ \hline \multirow{3}{*}{[107]} & \multirow{3}{*}{LIME} & \multicolumn{2}{c}{Explains the prediction of any classifier model with ease while ensuring local fidelity with speed} & Has the flexibility to be applicable to different models such as random forests and image classification & \multirow{3}{*}{2016} \\  & & \multicolumn{2}{c}{} & \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \hline \multirow{3}{*}{[108]} & \multirow{3}{*}{TRUST} & \multicolumn{2}{c}{To ensure fast and accurate XAI numerical applications using factor analysis to transform input features} & Model-agnostic, and high-performing, applicable to numerical applications & \multirow{3}{*}{2021} \\  & & \multicolumn{2}{c}{} & \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \hline \multirow{3}{*}{[109]} & \multirow{3}{*}{LORE} & \multicolumn{2}{c}{Backbox outcome explanation} & \multicolumn{2}{c}{Agnostic approach to learning the local interpretable predictor for decision rule-based explanations} & \multirow{3}{*}{2018} \\  & & \multicolumn{2}{c}{} & \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \hline \multirow{3}{*}{[110]} & \multirow{3}{*}{GRAD-CAM} & Heat map for each class of a single image & Uses the feature maps produced by the CNN. It is model-specific & \multirow{3}{*}{2019} \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \hline \multirow{3}{*}{[111]} & \multirow{3}{*}{CEM} & Leverages the features needed to predict that input instance are of same class & Agnostic model, local and for post hoc explanations & \multirow{3}{*}{2018} \\ \cline{3-5}  & & \multicolumn{2}{c}{} & \\ \hline \hline \end{tabular}
\end{table}