## 6 Results and Discussion  
In this section, the results of the experiments performed on the \"CCCS-CIC-AndMal-2020\" dataset are interpreted and discussed. The results obtained in the dataset are presented in Table 3. According to the test results, the XGBoost algorithm showed the most successful performance among the classifiers used without hyperparameter optimization with the AGA, with an accuracy rate of 99.82%. The second-best performance was the RF algorithm with an accuracy rate of 98.72%. Looking at Table 3, the SVM obtained the most unsuccessful results. The accuracy rate of the SVM algorithm was found to be 73.07%. The NB, KNN, and LR algorithms have obtained similar accuracy values in the range of 89-90%. The GB, ANN, and DNN achieved accuracy values of over 90%. The accuracy values of the GB, ANN, and DNN algorithms are 96.32%, 91.17%, and 93.82%, respectively. As a result of the hyperparameter optimization performed using the AGA technique, the XGB-AGA showed the most successful result. The XGB-AGA showed the most successful performance of all the experiments with its 99.82% accuracy rate performance. At the same time, the results obtained with the RF-AGA and NB-AGA algorithms show that the use of the AGA increases the performance positively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline NB & 0.891 & 0.891 & 0.8942 & 0.891 \\ RF & 0.9742 & 0.9742 & 0.9743 & 0.9742 \\ KNN & 0.8982 & 0.8982 & 0.903 & 0.8979 \\ LR & 0.8789 & 0.8789 & 0.8806 & 0.8787 \\ SVM & 0.7307 & 0.7307 & 0.7486 & 0.7257 \\ GB & 0.9632 & 0.9632 & 0.9632 & 0.9632 \\ ANN & 0.9117 & 0.9117 & 0.9117 & 0.9117 \\ XGBoost & 0.9735 & 0.9735 & 0.9736 & 0.9735 \\ DNN & 0.9382 & 0.9382 & 0.9382 & 0.9382 \\ NB-AGA & 0.9286 & 0.9283 & 0.9328 & 0.9289 \\ RF -AGA & 0.9872 & 0.9864 & 0.9891 & 0.9872 \\ XGB-AGA & 0.9982 & 0.9978 & 0.9985 & 0.9982 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of all algorithms. The graphical representation of the accuracy, recall, precision, and F-score values of the classifiers used in the experiments is shown in Figure 7.  
Confusion matrices showing the estimates obtained on the test set are shown in Figure 8 for each classifier. The \"0\" index of the confusion matrix indicates benign samples, and the \"1\" index indicates malware samples. In binary classification problems, the estimation of both classes is important. However, the \"FN\" parameter, which indicates the number of samples that the classifier estimates as benign but is actually malware, is of vital importance in the safety of autonomous vehicles. The XGB-AGA classifier made only 6 false detections for 2800 test samples. The \"FP\" number of the XGB-AGA classifier is 4. It is seen that the \"FN\" and \"FP\" numbers of the NB, KNN, SVM, and LR algorithms are high and these classifiers make a high amount of incorrect predictions. Although the \"FP\" number of the GB algorithm is higher than the RF, the \"FN\" number is lower than the RF. It is also noteworthy that the \"FN\" number of the XGBoost classifier without using the AGA is lower than that of the RF-AGA classifier. The \"FN\" and \"FP\" predictions of the DNN classifier, which is different from the classical machine learning methods, are almost equal and this shows that it makes similarly accurate predictions for both classes. The ROC curve shows the ability of the classifier to discriminate between the two classes. The area under the curve shows the AUC score. The larger this area, the more developed the classifier's discrimination ability. Figure 9 shows the ROC curve and AUC scores of each classifier used in the experiments. Looking at Figure 9, it is seen that the XGB-AGA classifier exhibits the most successful discrimination ability with an AUC score of 0.99. Secondly, the RF-AGA classifier obtained an AUC score of 0.98. The RF and XGBoost obtained an AUC score of 0.97, the GB 0.96, and the DNN 0.94. The KNN, LR, NB, and SVM classifiers performed at 0.90 and below.