## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Datasets Used for IoV IDS  
The evaluation of ensemble learning-based multi-class IDS for IoV heavily relies on the quality and variety of datasets used. These datasets are essential for training, validating, and testing IDS models, ensuring their robustness and reliability in real-world scenarios. Various datasets have been employed in existing research, each offering unique attributes and challenges. Figure 6 is a pictorial view of the dataset's sizes. Table 5 summarizes the datasets used in IoV intrusion detection. These datasets provide a comprehensive basis for the evaluation of various detection models. They address specific challenges and scenarios pertinent to vehicular networks, enhancing the relevance and applicability of the research findings. Figure 5: Detection and False Rates. Figure 6: Dataset Sizes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Algorithms** & **Detection** & **False Alarm Rate (\%)** & **Key Insights** & **Practical Implications** \\ \hline \multirow{3}{*}{[57]} & Random Forest, GBM, AdaBoost & \multirow{3}{*}{98.7} & \multirow{3}{*}{2.3} & High accuracy reduces false positives. & Effective in dynamic IoV environments, computationally intensive \\ \cline{1-1} \cline{5-5}  & Federated & & & & \\ \cline{1-1} \cline{5-5}  & Ensemble & \multirow{3}{*}{97.9} & \multirow{3}{*}{3.1} & Enhance privacy and scalability & Suitable for decentralized systems, challenges in coordination \\ \hline \multirow{3}{*}{[59]} & Hyper-Tuned & \multirow{3}{*}{99.2} & \multirow{3}{*}{1.8} & High accuracy, reduced false positives. & Effective for real-time detection, scalability concerns \\ \cline{1-1} \cline{5-5}  & RF, CNN & & & & \\ \cline{1-1} \cline{5-5}  & ensembled & \multirow{3}{*}{98.5} & \multirow{3}{*}{2.5} & Robust detection reduces false positives. & Robust in handling high-dimensional data, real-time challenges \\ \cline{1-1} \cline{5-5}  & together & & & & \\ \hline \multirow{3}{*}{[75]} & DNNs, GNNs & \multirow{3}{*}{99.0} & \multirow{3}{*}{1.9} & High accuracy, detects complex patterns. & Suitable for complex attack patterns, scalability issues \\ \cline{1-1} \cline{5-5}  & Machine & & & & \\ \cline{1-1} \cline{5-5}  & Learning-Based & \multirow{3}{*}{97.3} & \multirow{3}{*}{3.2} & Improved routing efficiency & Effective for VANET routing, implementation complexity \\ \cline{1-1} \cline{5-5}  & Routing & & & & \\ \hline \multirow{3}{*}{[79]} & Graph Node & \multirow{3}{*}{98.8} & \multirow{3}{*}{2.2} & High accuracy identifies complex relationships. & Effective feature extraction, scalability issues \\ \cline{1-1} \cline{5-5}  & Network & & & & \\ \hline \multirow{3}{*}{[50]} & GRIPCA, & \multirow{3}{*}{99.1} & \multirow{3}{*}{1.7} & High detection accuracy, efficient learning & Suitable for real-time detection, implementation complexity \\ \cline{1-1} \cline{5-5}  & OWELM & & & & \\ \hline \multirow{3}{*}{[58]} & LSTM-based & \multirow{3}{*}{97.5} & \multirow{3}{*}{1.9} & Combines LSTM with hybrid methods for improved detection accuracy & Demonstrates potential for high accuracy in detecting complex intrusion patterns in IoV environments \\ \cline{1-1} \cline{5-5}  & LSTM-based & & & & \\ \cline{1-1} \cline{5-5}  & hybrid IDS & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Ensemble Learning-based Multi-Class IDS Performance Comparison.