## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Ensemble Learning-Based Multi-Class IDS for IoV  
By analyzing the provided taxonomy, we conclude that there is still a notable gap in existing research, specifically, it is still challenging to create a scalable, lightweight IDS that can effectively operate in dynamic IoV contexts. In order to meet the requirements of real-time detection and computing limitations, it is imperative for future research to prioritize the enhancement of ensemble learning algorithms. Moreover, it is essential to preserve and enhance databases to accurately reflect the latest threat scenarios. [PERSON] et al. [57] applied ensemble learning through Random Forest, Gradient Boosting Machine, and AdaBoost to build a multi-class IDS tailored for dynamic IoV environments. Their model achieved a detection rate of 98.7% and effectively minimized false positives (2.3%), proving the strength of ensemble methods in reducing misclassification, though at the cost of increased computational complexity. In 2021, [PERSON] et al. [72] created a hybrid IDS by combining known and zero-day attack detection mechanisms, achieving 97.2% accuracy. The ensemble demonstrated balanced precision and recall across multiclass labels, supporting hybrid approaches for comprehensive IoV protection. [PERSON] et al. [62] introduced a clock-skew-based ensemble IDS, achieving 98.1% detection with 1.5% false alarms. Their novel approach emphasizes real-time synchronization signals for anomaly detection, offering lightweight yet reliable solutions for time-critical vehicular applications. However, [PERSON] and [PERSON] [58] addressed intrusion detection through machine learning-based routing in VANETs. The ensemble model attained a detection accuracy of 97.3%, improving traffic flow efficiency while detecting anomalies. However, the method requires complex integration with underlying routing protocols, posing deployment constraints. [PERSON] et al. [59] advanced the field with a hyperparameter-tuned Random Forest ensemble, achieving a leading 99.2% detection rate and 1.8% false alarm rate. Their study proves that careful parameter optimization significantly enhances real-time intrusion detection performance, particularly in latency-sensitive vehicular networks. Random Forest has been ensembled with CNN to detect diverse intrusion types in high-dimensional vehicular traffic [69]. The researchers' model reached 98.5% detection accuracy with a 2.5% false alarm rate, highlighting the robustness of hybrid architectures in handling feature-rich inputs, though real-time deployment remains challenging. The authors in [58] integrated LSTM with hybrid ensemble learning for sequential data processing. Their 97.5% detection rate and 1.9% false alarm rate demonstrate that temporal modeling in IDSs can significantly improve anomaly detection in complex vehicular patterns. [PERSON] et al. [48] implemented a stacked ensemble tailored for software-defined VANETs. Despite achieving 97.5% accuracy and 1.8% false alarms, the model faces difficulties with real-time responsiveness, underscoring the tradeoff between depth of learning and execution time. [PERSON] and [PERSON] [45] proposed a federated IDS integrated with satellite mesh networks, achieving 95.9% accuracy. Their design prioritizes scalability and privacy in multi-class detection but remains hindered by the latency inherent in satellite communications. The researchers in [74] used a two-stage deep learning ensemble with DNNs to detect vehicular attacks with 97.9% accuracy. Their model shows strong generalization for various intrusion types, yet it requires intensive computational tuning and dataset balancing strategies. [PERSON] and [PERSON] [42] presented a highly optimized ensemble combining LightGBM, Extra Trees, and XGBost. With a detection rate of 98.2%, their model showcases superior accuracy and low latency performance, reflecting the power of hyperparameter-driven ensemble design. In [53], the authors proposed a collaborative cloud-vehicle IDS architecture based on ensemble learning.