## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Datasets Used for IoV IDS
Table 7 provides a structured overview of the datasets, facilitating easy comparison and helping in the selection of appropriate datasets for IoV IDS research. From the literature and back to 5 years, Figure 7 represents the usage percentage of each dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Algorithms** & **Detection Rate (\%)** & **False Alarm Rate (\%)** & **Key Insights** & **Practical Implications** \\ \hline
[51] & DL, adaptability frameworks & 97.4 & 1.8 & High adaptability and detection accuracy for zero-day attacks & Points out the effectiveness of adaptable DL methods in managing emerging cyber threats \\ \hline
[46] & Logistic Regression, Random Forest, DT & 97.3 & 1.4 & Improved detection accuracy through extensive hyperparameter optimization & Shows the necessity of parameter tuning for achieving high performance in IDS \\ \hline
[72] & Hybrid IDS & 97.2 & 1.5 & High precision in detecting known and unknown attacks through hybrid detection techniques & Shows the effectiveness of hybrid methods in enhancing detection capabilities \\ \hline
[62] & Clock skew analysis, real-time detection & 98.1 & 1.5 & Effective real-time detection with minimal latency through clock skew analysis & Highlights the potential of clock skew analysis in achieving real-time intrusion detection in-vehicle networks \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 7: Datasets Usage Percentage for IoV IDS. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Advantages**} & \multirow{2}{*}{**Disadvantages**} & \multicolumn{2}{c}{**Usage Rate with Ensemble**} & \multicolumn{2}{c}{**Learner Performance**} & \multirow{2}{*}{**Targeted Attacks**} & \multirow{2}{*}{**Last Update**} & \multirow{2}{*}{**Unique Attributes**} & \multirow{2}{*}{**Works**} \\  & & & & **Learning** & & & & & \\ \hline \multirow{6}{*}{KDD Cup 99} & Comprehensive & Outdated for & & & & & & Benchmark datasets \\  & coverage of & modern threats. & & Moderate & High accuracy & & are widely used in \\  & network & It contains & (benchmarking & but limited & General network & 1999 & IDS research. \\  & intrusion types. & records and & legacy IDS & real-world & intrusions. & Historical & [74,80] \\  & large dataset & imbalanced & models). & application. & & & representation of \\  & models. & classes. & & & & & attack patterns. \\ \hline \multirow{6}{*}{NSL-KDD} & Improved & Limited to older & High & & & & & Balanced and \\  & CV Pup 99, & Attack scenarios. & (benchmark & different from: performance; & & & & \\  & Balanced dataset & IoV-specific & balanced ML & supports better & intrusions. & 2009 & Reduced cup 99. \\  & or model & evaluation. & data. & models). & & generalization. & & \\ \hline \multirow{6}{*}{CICIDS 2017} & Includes & & & & & & & \\  & attack scenarios. & & & & & & \\  & Rich feature set & & & & & & \\  & for time-based & & & & & & \\  & analysis. & & & & & & \\ \hline \multirow{6}{*}{UNSW-NB15} & Rich and diverse & \multirow{2}{*}{
\begin{tabular}{c} Rich and diverse \\ attack scenarios. \\ Detailed feature \\ representation. \\ \end{tabular} } & High & & & High & & & & & Broad spectrum of \\  & & computational & Moderate & & & & & attack types. \\  & Detailed feature & requirements for & (complex IDS & supports & feature-rich & & & \\  & representation. & & & & ensemble & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{6}{*}{Car-Hacking} & Specific to & & & & & & & \\  & vehicular & Limited & High & & & & \\  & networks. & generalization to & Low (specialized & classification & & & \\  & Provides real & non-vehicular & automotive IDS). & accuracy for & network attacks & & \\  & CAN bus signals & scenarios. & & & & & \\  & for research. & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis of Common Datasets Used in IDS for IoV.