## 5 Discussion
Moreover, there is a significant disparity in the incorporation of ensemble learning with future technologies like edge computing, blockchain, and federated learning [84]. While additional research is necessary to comprehensively grasp the advantages of these technologies, they provide the potential to augment the security and resilience of IDS. Besides, in order to address the issue of shifting threats in IoV IDS, it is necessary to design adaptive algorithms that can respond immediately. This is because current ensemble learning techniques often struggle to adjust to these changes in a timely manner. Regarding privacy and security considerations [45; 57; 61], pose further obstacles to the application of ensemble learning IDS. It is a delicate task that requires further investigation to find a compromise between privacy concerns and the usefulness and efficiency of IDS. There is a continuous trade-off between the efficient use of resources and the level of precision. Efficiently balancing detection accuracy with little memory and CPU power usage continues to be a major challenge. An important exclusion is the absence of practical validation. Many research studies like [46; 59; 75] utilize controlled experiments and simulations, which fail to adequately replicate real-world situations. Field experiments and live deployments are essential for bridging this gap and showcasing the effectiveness of ensemble learning IDS in practical IoV scenarios. Moreover, the process of modifying hyperparameters in ensemble learning methods can be both time-consuming and perhaps unachievable in real-time situations. In order to expedite the procedure, it is necessary to do research on automated and efficient approaches for hyperparameter optimization. As understood from [59; 82], the creation of comprehensive security frameworks is insufficient. Although there has been significant study on individual components of IDS, ensuring adequate protection for the IoV requires complete frameworks that integrate ensemble learning IDS with other security measures. To enhance the practicality and effectiveness of ensemble learning-based IDS in the IoV, it is crucial to address these research gaps. Additionally, this will contribute to the establishment of more dependable and secure automotive networks. Despite the breadth of this survey, several limitations are acknowledged that open avenues for further research. One key limitation lies in the availability and diversity of benchmark datasets. Many of the reviewed studies rely heavily on datasets such as NSL-KDD and CICIDS2017, which, while popular, may not fully reflect the complexity, heterogeneity, and real-time dynamics of modern IoV environments. This lack of real-world data constrains the generalizability of reported performance metrics and limits the ability to validate models under evolving attack patterns or decentralized vehicular scenarios. Additionally, while ensemble methods show impressive accuracy and robustness, their computational overhead and scalability remain important concerns--especially in latency-sensitive and resource-constrained IoV deployments. Techniques like boosting and stacking, for instance, can incur training costs or inference delays that hinder real-time application at the edge. Moreover, few studies address adversarial resilience or privacy-preserving training, which are increasingly critical for secure IoV systems. These limitations suggest that future work should not only explore novel architectures but also prioritize lightweight, adaptive, and privacy-aware IDS frameworks that can operate reliably in realistic and distributed vehicular environments.