## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
These networks rely on intricate and dynamic communication structures, which make them highly vulnerable to various cyber threats, including DoS and Sybil attacks. Ensemble learning, through methods like RF and Gradient Boosting, combines multiple machine learning algorithms to enhance the accuracy and robustness of IDS. Unlike traditional IDS models that rely on static, pre-defined feature sets, ensemble approaches adapt dynamically to emerging attack patterns and evolving vehicular traffic conditions. This adaptability ensures a more reliable defense mechanism for VANETs, as demonstrated in findings. Another research has shown that several ensemble strategies are useful in IDS for IoV. [PERSON] and [PERSON] [42] suggested that utilizing a combination of classifiers, namely XGBoost, Extra Trees (ET), and LightGBM, significantly improves the detection performance. [PERSON] and [PERSON] [43] argued that non-tree-based ensemble algorithms can efficiently detect intrusions in self-driving automobile systems. However, the complex and diverse structure of automobile network traffic may sometimes exceed the capabilities of traditional single-model approaches. Ensemble methods are a favorable choice for IDS in IoV scenarios as they integrate many learning algorithms that are fine-tuned using methods such as Bayesian Optimization. By enhancing the precision of intrusion detection, this approach also reduces the frequency of false positives. In [44], the authors highlight the importance of feature extraction and selection in improving the performance of ensemble learning models. Data imbalance concerns, which are frequently encountered in IDS datasets, can be efficiently resolved by employing correlation-based feature selection and synthetic minority oversampling techniques (SMOTE). Furthermore, [PERSON] and [PERSON] [45] acknowledge the potential for system delays but contend that the implementation of federated ensemble learning in IDS has the potential to improve both privacy and scalability. However, other researchers suggest that optimization techniques like GA and Particle Swarm Optimization (PSO) can be used to adjust model parameters, thereby improving the overall efficiency of IDS [46; 71]. Additional studies are required to fully comprehend the capabilities of ensemble-based IDS in terms of scalability and real-time use in vehicular networks with restricted resources. The discourse over the application of DL methodologies in ensemble situations is significant. The research carried out in [48; 49] showcases the application of DNNs in extracting complex features from network traffic data. However, the substantial computational expense and extended training period of DNNs greatly hinder real-time intrusion detection. According to [PERSON] et al. [72], the combination of DNNs with ensemble methods can effectively utilize the advantages of both approaches, resulting in improved accuracy and robustness in IDS. [PERSON] et al. [73] expressed disapproval of the use of static datasets in previous research and advocate for the adoption of adaptive learning techniques that can adapt to evolving threat scenarios. Thus, while DL shows great potential, further improvement is needed to strike a balance between accuracy and efficiency before it can be effectively used in IDS for IoV. An example of a Multi-Class IDS using Tree-Based Ensemble Learning is demonstrated in Figure 3. The methods used excel in handling high-dimensional vehicular data by aggregating multiple DTs to improve classification accuracy. In a multi-class context, the IDS categorizes network behavior into benign activities and multiple types of malicious attacks, such as DoS, Sybil, and spoofing attacks. Tree-based ensemble methods are particularly effective due to their ability to capture complex feature interactions while minimizing overfitting. Additionally, techniques like feature importance and pruning ensure that the system remains computationally efficient, which is critical for real-time deployment in dynamic VANETs and IoV settings.