## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
Their model achieved 99.99% accuracy on two benchmark datasets and effectively addressed data imbalance using the Synthetic Minority Oversampling Technique (SMOTE)-Tomek method. This work highlights how stacked models combined with robust feature engineering can significantly boost detection performance in IoV environments. [PERSON] et al. [56] introduced a federated ensemble learning model incorporating class-incremental learning to ensure continuous adaptation to evolving threats in IoV. Their work emphasizes the importance of lifelong learning strategies in IDS and achieves consistent accuracy (~97%) with reduced false positives. This makes it well-suited for highly dynamic vehicular networks that require evolving defense mechanisms. In the same year, other researchers developed a hybrid ensemble IDS using optimization algorithms like RFE, SAEO, and PSO, integrated with classifiers such as Adaboost, SVM, DNN, and XGBoost [67]. Their comprehensive ensemble pipeline significantly boosts precision, sensitivity, and F1-score. Despite its computational demands, the system achieves greater than 99% accuracy, demonstrating its strength across multiple benchmark datasets. [PERSON] et al. [63] designed an ensemble learning system that leverages gossip learning in distributed V2X environments. Their IDS model enhances scalability and robustness by facilitating knowledge sharing between nodes in the network. The approach demonstrated improved detection performance and resilience in vehicular communication systems, though communication overhead remains a challenge. Meanwhile, other researchers proposed a tree-based ensemble framework tailored for multi-class IDS in IoV [60]. Their model integrates multiple decision tree algorithms to classify network traffic into distinct attack categories with high precision. The work stands out for its interpretability and effectiveness across multiclass detection tasks, particularly in environments requiring rapid decision-making under constrained resources. Federated learning with diverse ML models was combined in [65] in a decentralized architecture designed for privacy-preserving IDS in IoV. Their approach integrates SMOTE, outlier detection, and hyperparameter tuning to adapt to class imbalance and noisy data. The model is notable for its ability to scale across devices while maintaining strong detection performance and data confidentiality. With transfer learning and hyperparameter tuning, researchers introduced a lightweight IDS model using MobileNetV2 [64]. Their approach caters to resource-constrained IoV environments by balancing detection accuracy and processing efficiency. The model showed strong performance in intrusion detection tasks, although it may need further evaluation against sophisticated attack vectors. Previous studies on IoV intrusion detection, particularly the analysis of various ensemble learning techniques as illustrated in Table 4, have shown through the works in [52; 68] that these strategies significantly enhance the accuracy of identification while reducing the incidence of false positives. Nevertheless, additional obstacles persist, such as the necessity for rapid, flexible solutions and the creation of models that can react to emerging security issues. Particularly via ensemble approaches like RF, Gradient Boosting Machines (GBM), and AdaBoost. Developments in DL and ML have significantly raised the efficacy of IDS for the IoV [57; 69]. Notwithstanding these developments, IDS's ability to meet the changing and immediate needs of IoV environments remains limited. Computational restrictions and delays impede their use in time-sensitive applications, particularly in systems like Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs). Optimization techniques such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) [70] would help to improve the scalability and efficacy of ensemble models. [PERSON] et al. [44] highlighted the application of ensemble learning in developing IDS for Vehicular Ad Hoc Networks (VANETs).