## 4 Results and Discussion
### Performance Assessment of Classifiers  
The mean Accuracy, Precision, Recall, and F1-score of each classifier are reported in Table 4  
From Table 4, we notice that both ARF and KNN classifiers achieve good performance. However, Adaptive Random Forest (ARF) outperforms the KNN classifier in terms of Accuracy, Precision, Recall, and F1-score. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Incremental Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Adaptive Random Forest (ARF) & 94.90\% & 93.10\% & 96.81\% & 94.92\% \\ KNN Classifier & 87.38\% & 87.58\% & 86.62\% & 87.09\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The mean performance of ARF and KNN classifiers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & \multicolumn{4}{c}{**Pretrain_Size**} \\ \cline{2-5}  & **400** & **600** & **1000** & **1200** \\ \hline Adaptive Random Forest (ARF) & 94.73\% & 94.77\% & 94.83\% & 94.90\% \\ K-Nearest Neighbors classifier (KNN) & 86.86\% & 87.13\% & 87.30\% & 87.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The mean Accuracy of classifiers under varying pretrain size values. ARF reaches 94.90%, 93.10%, 96.81% and 94.92% for Accuracy, Precision, Recall and F1-score, respectively, while KNN records a lower performance: the Accuracy is 87.38%, Precision is 87.58%, Recall is 86.62%, and F1-score is 87.09%. These findings suggest that Adaptive Random Forest may be a more suitable choice for detecting malicious nodes in VANETs, offering improved predictive capabilities compared to the KNN algorithm. Complementing the above results, the variations of the performance of both classifiers over time according to each performance metric are plotted. Figures 5-8 illustrate the monitoring over time of the performance of the classifiers regarding Accuracy, Precision, Recall, and F1-score, respectively. For each performance metric, the mean and the current values are plotted. We note that the mean Accuracy is obtained by averaging the Accuracy scores obtained in all test steps while the current Accuracy refers to the Accuracy of the classifier on the current test step. For each classifier, Figures 5-8 exhibit both the mean and the current performance over time. Figure 5 represents the monitoring of Accuracy for the ARF and KNN algorithms. First, we notice that the Accuracy of ARF is higher than that of KNN throughout the evaluation time, with a mean Accuracy ranging from 93% to 95% for ARF while KNN records a lower performance with a mean Accuracy between 83% and 87%. Further, we observe that the plots of the mean Accuracy for both ARF and KNN appear to be stable throughout the evaluation time. This outcome is logical since the mean Accuracy is an average measure which computes the overall performance of the classifier over multiple test periods. These outcomes suggest that both classifiers show consistent Accuracy over time. Similarly, examining the plots of the current Accuracy of both ARF and KNN, we point out that ARF achieves a higher Accuracy between 92% and 97%, outperforming KNN, whose current Accuracy ranged from 83% to 91%. Figure 8: F1-score of ARF and KNN classifiers over time. Figure 7: Precision of ARF and KNN classifiers over time. Overall, the plots of current Accuracy present more variations in comparison with those of mean Accuracy. This is because current Accuracy reflects the performance of the classifier on the most recent test period of time. Considering the above results, we can conclude that monitoring both mean and current Accuracy over the evaluation time can be useful in monitoring the real-time performance of the classifiers and provide a more reliable estimate of the classifier's ability to detect misbehavior. Similarly, Figures 6-8 capture the variations of Recall, Precision, and F1-score metrics over time. Both the plots of mean and current metric are monitored. ARF shows superior performance in comparison with KNN regarding all metrics, namely Recall, Precision, and F1-score.