## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Ensemble Learning-Based Multi-Class IDS for IoV
Their model achieved 97.6% detection with strong adaptability, offering a viable path for distributed multi-class detection frameworks with minimal onboard processing requirements. [PERSON] et al. [44] employed correlation-based feature selection and SMOTE in their ensemble, achieving 97.8% detection. Their modelshows strong resilience to data imbalance, a common challenge in real-world IoV datasets, and provides a robust baseline for preprocessing-focused IDS design. The authors in [51] introduced a DL-based adaptable ensemble framework capable of detecting zero-day attacks with 97.4% accuracy. Their contribution lies in the model's continuous learning capacity, making it highly suitable for evolving threat environments. [PERSON] and [PERSON] [46] conducted a comparative study of Logistic Regression, Random Forest, and Decision Trees within an optimized ensemble, achieving 97.3% accuracy. Their findings validate the role of extensive hyperparameter tuning in boosting classifier robustness for real-time IoV IDS. [PERSON] and [PERSON] [45] emphasized the impact of feature reduction through RFE and PCA in ensemble IDSs. Achieving 96.7% detection, their model demonstrates that careful dimensionality reduction can boost classifier performance without sacrificing generalization. Recently, [PERSON] and [PERSON] [78] proposed a federated ensemble learning framework that integrates distributed learners across edge nodes to maintain privacy in decentralized IoV systems. With a detection rate of 97.9%, the approach demonstrated strong scalability and data confidentiality, despite facing coordination and communication overheads during model synchronization. Researchers in [75] introduced a deep ensemble combining DNNs and Graph Neural Networks, designed for IoV scenarios with complex relational data. Their system recorded a 99.0% detection rate and 1.9% false alarm rate, validating the effectiveness of graph-based learning in capturing spatial-temporal dependencies of vehicular threats. [PERSON] et al. [79] employed a Graph Node Attention Network for multi-class detection, achieving 98.8% detection with only 2.2% false alarms. Their method excels in extracting topological and contextual traffic features in large-scale vehicular graphs, offering an interpretable yet scalable solution for next-gen IDS. [PERSON] et al. [50] proposed GRIPCA combined with OWELM to deliver a lightweight and fast IDS, suitable for real-time inference. Their model reached 99.1% accuracy and 1.7% false alarms, emphasizing both detection effectiveness and efficient resource consumption, a valuable tradeoff for embedded vehicular systems. [PERSON] [43] evaluated non-tree-based ensemble techniques in IoV IDS. Their model achieved a 96.5% detection rate with improved precision and reduced false positives, presenting a compelling alternative to tree-heavy ensemble paradigms in adversarial environments. [PERSON] et al. [52] proposed sensory anomaly detection through ensemble methods, reaching 96.9% detection. Their study underscores the importance of mixed sensory data in automotive environments and calls for more sophisticated ensemble handling of sensor fusion. [PERSON] et al. [49] focused on boosting-based ensemble learning, leveraging hyperparameter tuning to achieve 98.0% detection accuracy. Although efficient, real-time adaptation remains a bottleneck, especially under varying vehicular traffic conditions. [PERSON] et al. [54] applied ensemble learning to traffic congestion detection in VANETs, achieving 96.4% accuracy. While their approach focuses more on traffic prediction than intrusion detection, it demonstrates the versatility of ensemble frameworks in vehicular systems. Figure 5 summarizes the performance of the existing ensemble learning-based Multi-Class IDSs for IoVs.