## 5 Discussion
The Car-Hacking Dataset prioritizes vehicular network security and highlights the significance of certain datasets in research related to IoV. Studies emphasize the significance of accurately replicating actual automotive attacks by utilizing numerous CAN bus signals [75; 77]. Our research indicates that datasets designed specifically for automotive networks are highly helpful for the development of IDSs that can effectively protect against threats unique to autos. An important practical consequence is the potential for IDS systems that are more targeted and effective in dealing with the specific difficulties of IoV. The study's overall findings underscore the significance of using diverse and reliable information for designing IoV-specific IDS. The results indicate that contemporary, specialized datasets are crucial for enhancing the significance and longevity of IDS models, while conventional datasets offer valuable benchmarking capabilities. These findings highlight the significance of regularly updating datasets and creating tailored datasets to adapt to the changing threat landscape in IoV. This has substantial implications for future research and implementation. The challenges of getting big, diverse datasets for training and validation, particularly in proprietary or specialist environments like vehicle networks, impede model generalization and efficacy. The reliance on DL and optimization algorithms raises questions about computing overhead, efficiency, and the viability of deployment in resource-constrained contexts. Collectively, these constraints highlight the gap between theoretical model performance and real-world application, indicating a vital area for future study to reconcile theoretical improvements with practical, scalable solutions to cybersecurity concerns in automotive and IoT systems. We also pointed out that two important options for enhancing scalability and privacy are federated ensemble learning and distributed IDS frameworks. [PERSON] and [PERSON] [46] highlighted the advantages of using federated ensemble learning-assisted IDS to safeguard autonomous cars over mesh satellite networks. They stressed the improved scalability and privacy provided by this approach. However, in their study, they investigated the effectiveness of intricate boosting strategies, highlighting their precision as well as the challenge of managing network traffic in real-time [49]. [PERSON] et al. [53] suggested a collaborative IDS that connects cloud and vehicle systems. They highlighted the significance of achieving greater accuracy in detecting intrusions and the ability to adjust in real-time. Nevertheless, although these solutions exhibit promise, they also illustrate the arduous and resource-demanding nature of developing advanced IDS systems in IoV environments. Further comprehensive research is needed to enhance our understanding and establish connections between experimental results and feasible, scalable solutions for IoV security. By referring to the studied literature and despite the existence of research gaps that now prevent the complete integration of ensemble learning into IDS for the IoV, this review demonstrates considerable potential in addressing this issue. The scalability of ensemble methods poses a major challenge. While these methods demonstrate efficacy in controlled settings, their effectiveness diminishes when applied to real-world situations, including extensive IoV implementations. Effective scaling is necessary to manage the substantial volumes of data produced by several autos and intricate network architectures. We find that real-time processing poses another key challenge marked in [78], it is the computational complexity which is inherent in many ensemble learning techniques and may prevent them from effectively processing data in real time, which is crucial for rapid threat detection and response on IoV. To overcome this difficulty, it will be necessary to develop algorithms that are lightweight, efficient, and have a high level of detection accuracy.