## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Datasets Used for IoV IDS
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Description** & **Size** & **Features** & **Work** \\ \hline \multirow{2}{*}{KDD Cup 99} & Contains a wide range of network intrusion types & 4,898,431 & 41 features, multiple attack types & [74; 80] \\ \hline \multirow{2}{*}{NSL-KDD} & An improved version of KDD Cup 99 with fewer duplicates & 125,973 & 41 features, balanced data & [81; 82] \\ \hline \multirow{2}{*}{CICIDS 2017} & Includes benign and up-to-date common attack scenarios & 2,830,743 & 80 features, time-based analysis & [69; 83] \\ \hline \multirow{2}{*}{UNSW-NB15} & Provides modern normal and attack behaviour & 2,540,044 &
\begin{tabular}{c} 49 features, diverse attack scenarios \\ \end{tabular} & [57; 59] \\ \hline \multirow{2}{*}{Car-Hacking Dataset} & Focused on vehicular network security & 3,000,000 & Multiple CAN bus signals & [75; 77] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Common Datasets for IoV IDS. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Algorithms** & **Detection Rate (\%)** & **False Alarm Rate (\%)** & **Key Insights** & **Practical Implications** \\ \hline
[48] & Stacked ensemble & 97.5 & 1.8 & High detection accuracy with stacked ensemble methods & Highlights computational challenges for real-time processing due to high demands \\ \hline
[45] & Federated learning, mesh satellite nets & 95.9 & 2.5 & Enhanced scalability and privacy in IDS deployment through federated learning in IoV \\ \hline
[73] & Deep neural networks, two-stage classification & 97.9 & 1.6 & High classification accuracy for smart vehicle networks & Highlights the potential and challenges of using DL for IDS in smart vehicle networks \\ \hline
[43] & Non-tree-based ensemble methods & 96.5 & 2.1 & Higher precision with reduced false positives and improving detection accuracy \\ \hline
[71] & Recursive & 96.7 & 2.0 & Enhanced performance through dimensionality reduction & Demonstrates the role of feature reduction in improving IDS efficiency \\ \hline
[84] & Sensory anomaly detection, ensemble methods & 96.9 & 2.3 & Improved detection rates for sensory data & Emphasizes the importance of handling mixed sensory data in connected and automated vehicle networks \\ \hline
[49] & Boosting & 98.0 & 1.7 & High accuracy with challenges in handling real-time network traffic with advanced boosting methods \\ \hline
[42] & LightGBM, Extra Trees, XGBoost & 98.2 & 1.3 & Significant performance improvement through optimized ML-based IDS & Demonstrates the potential for enhanced security in IoV environments through advanced ML techniques \\ \hline
[53] & Collaborative learning, cloud-vehicle integration & 97.6 & 1.9 & Real-time adaptability, enhanced detection accuracy & Demonstrates the benefits of collaborative learning in achieving real-time adaptability in IDS for IoV \\ \hline
[54] & Machine learning algorithms, ensemble learning & 96.4 & 2.2 & Improved traffic congestion detection and prediction through machine learning & Highlights the role of machine learning in enhancing traffic management in VANETs \\ \hline
[44] & Feature selection, SMOTE & 97.8 & 1.6 & Improved accuracy and data imbalance handling through advanced feature selection and preprocessing & Emphasizes the importance of data through advanced preprocessing \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._Referring to what is being noted from the literature, high usage datasets like KDD Cup 99 and CICIDS 2017 are popular for their respective benchmarks in legacy and modern research. Moreover, specialized datasets like Car-Hacking have limited usage but are invaluable for automotive-focused studies. Legacy datasets (e.g., KDD Cup 99) are outdated but still referenced for benchmarking. Modern datasets like CICIDS 2017 and UNSW-NB15 provide recent attack patterns and feature engineering capabilities. Datasets like Car-Hacking offer domain-specific insights, while general-purpose datasets like CICIDS 2017 enable broad applicability.