## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps  
As connected cars become more common on our roads, keeping their networks safe is getting trickier. We need better ways to spot cyber threats, and that's where team-based learning approaches (i.e., ensemble learning) have been showing real promise. But looking closely at the current research, we can see some clear trends and gaps that need addressing if we want these security systems to work better and handle more cars. In their big review of the field, [PERSON] et al. [35] highlight a growing problem: most security systems for connected cars are just too heavy. They need tons of computing power, which makes it hard to spot threats in real-time--a big issue when you're dealing with fast-moving vehicles that can't afford delays. They suggest future research should focus on creating lighter, more efficient security solutions that can work quickly without sacrificing accuracy. This points to a major gap in current research--we desperately need security models that are lightweight and can scale up easily, adapting to the constantly changing, resource-limited environment of vehicle networks. In a similar vein, [PERSON] and [PERSON] [38] shine a spotlight on another challenge: making these systems harder to fool. Traditional security systems often struggle when faced with sophisticated attacks designed specifically to slip past their defenses. They recommend developing security models that can stand strong against these clever attacks and adapt to new threat patterns they haven't seen before. This matches the growing need for security systems that don't just work well under normal conditions but stay robust when facing evolving and unexpected attacks. These findings from both research teams highlight two key gaps that need filling:  
1. The need for lightweight models that can run efficiently in real-time on connected vehicles without hogging limited resources. 2. The need for systems that are resilient against sophisticated attacks, security that can take a punch and still do its job. In Table 4, we summarize and compare the different studies on ensemble learning for multi-class IDS for IoV. Recently, [PERSON] et al. [39] proposed an explainable ensemble transfer learning model aimed at detecting zero-day attacks in IoV systems. Their work stands out by combining ensemble learning with explainable AI (XAI) components, allowing not only for high detection accuracy but also for greater interpretability of decisions made by the model. The approach addresses key gaps in transparency for mission-critical security applications in vehicular networks. Moreover, [PERSON] et al. [40] introduced a hybrid ensemble architecture combining Feature-Augmented Convolutional Neural Networks (FA-CNN) with Deep Autoencoders for intrusion detection in NIDS. This model showed strong performance on NSL-KDD and CICIDS2017, especially in handling minority attack classes. While scalability remains a concern, the study provides valuable insights into deep hybrid ensemble design for high-dimensional security data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Techniques Used** & **Key Features** & **Advantages** & **Limitations** & **Key Contributions** & **Findings** \\ \hline \multirow{4}{*}{[39]} & Ensemble Transfer Learning, Explainable & Designed for detecting zero-day attacks in IoV environments with enhanced interpretability. & Improved detection of previously unseen attacks; provides explanations for model decisions of IoV, learning integration & Protential computational complexity due to model tailored for IoV, learning the underlying & Demonstrated high accuracy in identifying zero-day attacks with added model model \\  & Intelligence (XAI) & Interpretability.