## 5 Discussion  
These studies use a variety of approaches and datasets, but common constraints appear, highlighting the inherent issues of cybersecurity in automotive and IoT networks. These include investigating various real-world circumstances and hardware constraints that may impact the practical implementation and scalability of suggested models. Furthermore, numerous studies failed to mention their limits, highlighting a broader issue in cybersecurity research: the need for transparency and recognition of potential flaws. Another limitation found in various previous studies is the limited use of real data sets, which ensures scalability in the real world. Furthermore, relying on legacy datasets for training may limit generalizability across diverse and dynamic traffic scenarios. Furthermore, the studies lacked comparisons to previous research and novel models for detecting intrusion. This study aims to evaluate the effectiveness of ensemble learning for intrusion detection in Internet of Vehicles (IoV) environments. The findings provide an accurate overview \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Description** & **Equation** \\ \hline Accuracy & The proportion of correctly classified instances & \(Accuracy=\frac{TP+TN}{TP+TN+TP+TN}\) \\ \hline Precision & The proportion of true positive instances & \(Precision=\frac{TP}{TP+FP}\) \\ \hline Recall & The proportion of true positive instances & \(Recall=\frac{T_{P}}{TP+FN}\) \\ \hline F1-Score & The harmonic means of precision and recall & \(F_{1}Score=2\times\frac{precision\times Sensitivity}{Precision+Sensitivity}\) \\ \hline AUC-ROC & The area under the Receiver Operating & \\ \multirow{2}{*}{
\begin{tabular}{c} AUC-ROC \\ \end{tabular} } & Characteristic curve, measuring the model’s & \(AUC-ROC=\int\limits_{0}^{1}TPR\partial(FPR)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation Metrics. of the current state of the field and highlight key factors influencing the development and implementation of IDS in IoV systems. The results of our study indicate that the performance of IDS is greatly influenced by the choice of datasets. Although the KDD Cup 99 dataset is outdated, it remains an important resource due to its comprehensive coverage of many network intrusion types. This supports the argument made in [74] that performance measurements obtained from past data are valuable. Conversely, it can be argued that newer datasets like NSL-KDD offer a more precise representation of the current state of the network by resolving the problems of duplication and imbalance found in KDD Cup 99 [81]. The results indicate that whereas old datasets are valuable for comparing and evaluating performance, contemporary datasets are essential for creating models that can effectively address present attacks. The study emphasizes the significance of the CICIDS 2017 dataset in evaluating temporal attack trends due to its provision of up-to-date and accurate traffic conditions. The findings in [69] confirm the importance of time-based analytical characteristics in datasets such as CICIDS 2017 for detecting dynamic attack patterns in IoVs. These findings suggest that including such datasets in IDS evaluation can enhance the resilience of the model and improve its ability to detect real-world threats with greater accuracy. Moreover, the UNSW-NB15 dataset encompasses a diverse array of attack scenarios, which greatly facilitates the creation of dependable IDS models. To enhance the applicability of IDS models, [57; 59] highlights the importance of utilizing up-to-date datasets that accurately reflect the prevailing threat scenarios. Our research indicates that incorporating datasets like UNSW-NB15 can enhance the effectiveness of detection systems by enabling them to handle a broader spectrum of attack vectors. This conclusion indicates that for IDS models to remain pertinent and efficient, forthcoming research should give priority to varied and up-to-date datasets.