## 7 Conclusions  
The safety concerns of self-driving vehicles have increased with the increase in communications that could cause significant damage to vehicles. The damage occurs when any external device is connected, and this device is infected with any malicious code in the vehicles, and this connection happened through any external communication path. The malicious code or malicious program can enter the internal network of the vehicles and cause damage to them. In an integrated automotive environment, where answers must be evaluated in real time, high accuracy and speed are essential to identify harmful activities and prevent damage to vehicles. In this study, a machine learning-based detection approach using hybrid analysis-based particle swarm optimization and an adaptive genetic algorithm is presented for Android malware detection in auto-driving vehicles. The test of the proposed approach was carried out on the \"CCCS-CIC-AndMal-2020\" data. The hybrid features in the dataset were selected with the PSO algorithm and classified by machine learning algorithms. Random forest and XGBoost algorithms obtained the most successful classification results in the experiments. An adaptive genetic algorithm has been used for the hyperparameter optimization of these algorithms and the current accuracy rates have been increased. In the experiments, the XGBoost algorithm showed the best performance using the AGA with an accuracy rate of 99.82%. The random forest algorithm reached a 98.72% accuracy rate with the AGA. AGA-based classifiers proposed in this study were compared with different classifiers such as NB, KNN, SVM, LR, GB, ANN, and DNN. As a result, it has been observed that feature selection using PSO and hyperparameter optimization using an AGA positively improves performance in Android malware detection. At the same time, it was seen that the results obtained in this study were more successful than similar studies in the literature.  
In future studies, testing with more applications can be performed to protect autonomous vehicles from Android malware. In addition, with low computational cost  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Analysis Approach** & **Classification Techniques** & **Accuracy** \\ \hline [PERSON] et al. [10] & Static & CNN & 0.9660 \\ \hline [PERSON] et al. [11] & Static & SVM & 0.9510 \\ \hline [PERSON] et al. [41] & Static & CNN + ANFIS & 0.9466 \\ \hline [PERSON] [36] & Static & DNN & 0.9816 \\ \hline [PERSON] et al. [39] & Static & Linear Regression & 0.9516 \\ \hline [PERSON] et al. [40] & Static & Naive Bayes & 0.9110 \\ \hline [PERSON] [42] & Static & FL-BDE & 0.9933 \\ \hline [PERSON] et al. [43] & Static & GA-StackingMD & 0.9866 \\ \hline [PERSON] and [PERSON] [9] & Dynamic & Random Forest & 0.9290 \\ \hline [PERSON] et al. [44] & Dynamic & LSTM & 0.9370 \\ \hline [PERSON] et al. [45] & Dynamic & Weighted Voting (RF, KNN, MLP, DT, SVM, LR) & 0.95 \\ \hline [PERSON] et al. [12] & Hybrid & DNN & 0.9110 \\ \hline Our approach & Hybrid & XGB-AGA & 0.9985 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of results.  
classification models developed based on a 1D convolutional neural network, the performance can be increased without using PSO and AGA methods.  
The first author ([PERSON]) conducted experiments and wrote the manuscript, software. The second author ([PERSON]) was responsible for supervision, project administration, and correcting the direction of the work. The third author ([PERSON]) revised the manuscript, validation. All authors have read and agreed to the published version of the manuscript.  
The authors received no financial support for the research, authorship, or publication of this paper.  
Not applicable.  
We would like to thank Gazi University Mobile Security Laboratory (MobSecLab) and researchers for their valuable contributions to the experiments and technical support required for our study.  
The authors declare no conflict of interest.