## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Techniques Used** & **Key Features** & **Advantages** & **Limitations** & **Key Contributions** & **Findings** \\ \hline
[58] & LSTM-based hybrid IDS & Sequential data & Improved detection of sequential anomalies & Training complexity & Hybrid intrusion detection system for IoV & Improved detection of sequential anomalies \\  & & learning & models & & using LSTM & \\ \hline
[59] & Hyper-Tuned Random & Hyperparameter & Hyperparameter & & Novel hyper-tuned & High accuracy, reduced \\  & Forest & optimization, ensemble & & false positives & Scalability concerns & flexible basic & \\  & & methods & & & safety messages & \\ \hline
[60] & Tree-Based Ensemble & Combines multi-class classification with & High detection accuracy, interpretable results. & May lack scalability for large datasets. & Deepmodel a tree-based & Demonstrated superior \\  & Network & Combines multi-class classification with & High detection accuracy, interpretable results. & May lack scalability for large datasets. & ensemble for IoV IDS, \\  & Network & & & large datasets. & multi-class detection & \\  & & & & & multi-class detection & applications. \\ \hline
[61] & Privacy-preserving & Integrates federated & & & Combined federated & \\  & ensemble IDS & Learning for & & & learning with ensemble & \\  & processing & & while maintaining & & overhead in federated & \\  & & processing. & detection accuracy. & environments. & & detection in \\  & & & & & privacy-sensitive IoV & \\  & & & & & scenarios. & \\ \hline
[62] & Clock skew-based & Uses clock synchronization & Effective real-time & Limited evaluation on & Proposed a novel & \\  & ensemble IDS & features for anomaly & detection with minimal & & limited evaluation on & \\  & & detection. & & latency. & & utilizing ensemble & real-time detection with \\  & & & & latency. & & techniques for \\  & & & & & in-vehicle networks. & \\ \hline
[63] & Ensemble learning & Distributed IDS & & & & \\  & combined with gossip & framework for V2X & & & & \\  & learning. & environments. & & & & \\  & & & & & \\  & & & & & & \\  & & & & & \\  & & & & & & learning for V2X \\ \hline
[64] & Ensemble & & & & & \\  & Transfer learning with & & & & & \\  & MobileNetV2 and & & & & & \\  & hyper-parameter & & & & & \\  & optimization. & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Study** & **Techniques Used** & **Key Features** & **Advantages** & **Limitations** & **Key Contributions** & **Findings** \\ \hline  & Integrates federated & & & & & \\  & learning with diverse & & & & & \\  & machine learning & & & & & \\  & models across multiple & & & & & \\  & devices, which & Privacy-preserving, & Ensures data privacy, & & & & \\  & resembles an & decentralized & improves detection & Communication & & & \\  & ensemble-like & framework with robust & rates, and scales & overhead, complexity in & & \\  & architecture at the & handling of imbalanced & efficiently across IoV & integration. & & \\  & system level, & and noisy data. & devices. & & & \\  & SMOTE for class & & & & \\  & imbalance, outlier & & & & \\  & detection, & & & & \\  & hyperparameter tuning. & & & & & \\ \hline  & Improved Random & Optimized feature & High accuracy (\(-\)99\%), & & Introduced hybrid IRFC & Achieves high intrusion \\  & Forest Classifier (IRFC), & selection, & & & & \\  & extra Trees ensemble & ensemble-based & & suitable for real-time & Limited to single dataset, lacks & \\  & learning & architecture, tested on & & evaluation in live or & \\  & learning & CI-ClDS 2018 dataset & computationally & adaptive IoV scenarios & & \\  & & efficient & & & \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._A stacked ensemble model was developed in [41], leveraging multiple base classifiers including Random Forest, KNN, and XGBoost, coupled with advanced correlation-based and embedded feature selection.