## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Distributed Deployment
#### 4.1.8 Based on Generation Method  
Bagging  
Many models produced via bagging, sometimes known as bootstrapping aggregating, reduce variance and improve resilience. To control various vehicle data, [PERSON] and [PERSON] [74] used bagging-based ensemble models incorporating RF. This approach greatly lowered overfitting and improved the IDS's ability to find anomalies in large-scale IoV networks. By aggregating forecasts from numerous models, the study showed improved accuracy in spotting recognizable and somewhat familiar attacks. Using bagging inside an edge-computing architecture for IoV IDS, [PERSON] et al. [75] proposed a bagging-based model with lightweight properties for solving computational efficiency and scalability problems to enhance their usability with limited resources.  
Boosting  
Boosting improves general model accuracy by repeatedly combining weak classifiers and giving misclassified events more weight. Advanced persistent threats in IoV environments were found by [PERSON] and [PERSON] [69] using GB. Their approach steadily improved weak classifiers to achieve remarkable accuracy in spotting complex attack patterns. Still, the study noted that increasing real-time applications results in a computational cost. [PERSON] et al. [61] examined how AdaBoost might be used in IDS. This method guarantees effective communication among IoV nodes and improves detection accuracy, therefore addressing the two privacy and scalability concerns.  
Stacking  
Superimposing stacking combines predictions from several models using a meta-classifier, therefore utilizing the complementary benefits of different models. With predictions from SVM, DTs, and NN, [PERSON] et al. [57] established a stacking ensemble framework. Their system's meta-classifier reduced false positives significantly and improved its flexibility to new anomalies, therefore helping in dynamic IoV settings. Employing host- and network-level data, [PERSON] et al. [77] combined stacking into a hybrid IDS using the capabilities of distinct models, particularly for complex attack paths; their method improved detection coverage and precision. [PERSON] et al. [57] utilized a stacking ensemble approach with advanced feature selection to enhance intrusion detection. They achieved high performance with an accuracy of 99.99% on both NSL-KDD and CIC-IDS datasets, effectively handling imbalanced datasets using the SMOTE-Tomek technique. Moreover, [PERSON] et al. [41] were based on stacked ensemble frameworks with multiple base classifiers.  
Voting  
Voting produces final predictions by aggregating the results of numerous classifiers using weighted or majority votes. Voting ensembles with edge computing were used in 2024 in [75]. The authors combined efficient classifier predictions to enable real-time decision-making in resource-constrained environments. The voting system improved the individual mistake robustness of the model. [PERSON] and [PERSON] [74] combined models trained on centralized datasets via voting. Their results showed how voting preserved the simplicity of implementation while raising general detection accuracy.