## 2 Background
### Ensemble Learning Approaches Overview
#### 2.2.1 Boosting  
It is a sequential ML method in which the next model fixes the errors created by the one before it. Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Ada Boosting, LightGBM (LGBM), and Cat Boosting have shown improved performance in recent intrusion detection studies based on empirical evaluations reported in the literature [16; 17]. These methods aim at classifying difficult situations [16; 18]. Boosting in general may reduce bias, but overfitting could appear due to noisy data [18]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Challenge** & **Description** \\ \hline Vulnerability to Cyber-Attacks & High connectivity and data exchange among numerous entities make IoV susceptible to cyber-attacks such as data breaches, malware infections, man-in-the-middle attacks, and DoS attacks. \\ \hline Secure Data Management & Managing and protecting vast amounts of data generated by IoV from unauthorized access, ensuring data privacy, and safeguarding sensitive information related to vehicle locations and user identities. \\ \hline Dynamic and Decentralized Nature & The dynamic and decentralized nature of IoV networks complicates the implementation of robust security measures, making it difficult to promptly detect and mitigate security threats. \\ \hline Heterogeneity of IoV Components & Interoperability and compatibility issues due to various types of vehicles, communication protocols, and sensors, which can create security loopholes that attackers may exploit. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IoV Architecture Challenges. XGBoost  
XGBoost improves ML model performance and computing speed by building decision trees (DT) in parallel. XGBoost is an effective implementation of a GBML algorithm where stochastic gradient or tree boosting is used to develop a powerful ML technique that performs well in a wide range of complex situations [19]. Gradient Boosting (GB)  
GB evaluates the significance of each attribute after creating the boosted tree, employing a robust metric referred to as feature importance. This scoring approach assesses the significance of each feature in developing DTs for essential decision-making. Feature importance quantifies the significance of each attribute [20]. The relevance is determined by explicitly comparing and evaluating each attribute in the dataset against the others. The relevance of an individual DT is determined by the quantity of each attribute split point, weighted by the total number of observations from that node. This division is employed to improve the algorithm's efficacy and efficiency. AdaBoost  
AdaBoost is an iterative supervised learning algorithm that combines multiple predictions of weak classifiers [21]. The combination of updates, datasets, and voting is done using a weighted majority. To ensure this is successful, events that are difficult to classify are given priority above those that have already been adequately described. LightGBM (LGBM)  
The gradient hoist supports the LightGBM classification algorithm, known for its minimal processing load. A significant proportion of algorithms within the tree-based boosting family, including xgboost, utilize a presorting step for feature selection and splitting. Despite its significant effort and memory overhead, this presorting method can accurately identify the dividing point [22]. Cat Boosting  
CatBoost outperforms in handling categorical variables within heterogeneous datasets compared to other gradient boosting DT implementations, as it utilizes ordered target statistics and ordered boosting. It utilizes symmetric trees to achieve efficient prediction times [17]. In the CatBoost method, each subsequent tree is generated with a reduced loss compared to its previous one. It allows the definition of custom functions, utilizes categorical features directly and efficiently, and reduces the necessity for extensive hyperparameter tuning.