## 3 Related Work
Previous research focused on lightweight anomaly detection frameworks, minimizing adversarial risks, and IDS designed for protocols like CAN, while also addressing limitations such as significant processing requirements, insufficient publicly available datasets, and computational efficiency. Meanwhile, our study conducts a comprehensive examination and evaluation of ensemble learning methodologies in IDS for IoV. Our findings in \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & **Reviewed** & **Focus** & **Limitation** & **Recommendation** \\ \hline
[35] & 17 & Within the structure of IoV, the focus is on employing ML to ensure and enhance secure vehicular communications. & The significant processing requirements and changing environmental issues restrict the immediate application of proposed solutions. & The primary focus of future research should be on lightweight models and enhanced edge computing solutions for IoV. \\ \hline
[38] & 93 & Explores machine learning techniques for managing security issues in vehicular networks. & In vehicular applications, adversarial risks, increased latency, and computational costs present challenges for current ML models. & Future research should focus on adaptive vehicular networks. \\ \hline
[36] & 27 & It highlights the application of DL in the management of extensive data analytics for fully or partially autonomous vehicles. & The advancement and empirical validation of DL models for automotive applications is impeded by the lack of publicly accessible IoV datasets. & Providing open IoV data sources and employing advanced DL architectures such as memory-augmented networks and generative adversarial networks is essential. \\ \hline
[10] & 61 & The study explores machine learning and deep reinforcement learning approaches, including transformers and transfer learning, to accurately identify deficiencies in the CAN protocol. & Implementing advanced DL models in vehicle security is constrained by challenges related to computing efficiency and the scarcity of real-world CAN datasets. & Future research should focus on creating dependable public datasets and exploring energy-efficient DL techniques tailored for CAN-based IDS. \\ \hline
[11] & 22 & Blockchain-enabled Federated Learning (FL) for IoVs. & Limited real-world datasets and vulnerability to adversarial attacks. & Recommending robust consensus mechanisms, privacy-preserving techniques, and diverse testing frameworks. \\ \hline
[37] & 42 & Investigates IDS for in-vehicle networks, emphasizing hybrid, payload-based, and flow-based methodologies designed to protect CAN. & Present IDS methodologies often inadequately detect novel threats, and the absence of standardized testing datasets constrains their efficacy. & Prospective studies must concentrate on standardized datasets, enhanced detection of complex threats, and the development of efficient, lightweight IDS systems. \\ \hline This survey & 56 & This study presents a detailed performance analysis of key ensemble learning strategies bagging, boosting, stacking, and voting applied to multiclass intrusion detection problems within IoV. Various real-world datasets (e.g., CICIoTTV2022, TON_IoV, VeReMi) are used to benchmark these models across critical metrics such as accuracy, F1-score, precision, recall, AUC, and computational efficiency. Both baseline and optimized versions (e.g., with PSO, GA, Bayesian tuning) are included to reflect real-time applicability. & Although ensemble learning methods for multi-class IDS in the IoV are extensively investigated in this survey, it does not particularly address energy efficiency and lightweight implementation for limited IoV systems, a subject covered in some previous surveys. Moreover, adversarial resilience models examined in other works are not fully addressed in this work. & Future research should focus on synthesizing more benchmarks datasets that use ensemble learning methods for attack detection. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A Comparison of Our Survey with Existing Related Surveys.