## 5 Conclusions  
Due to the explosive growth in the current usage of mobile devices, the market share of the Android operating system, which powers these devices, has increased dramatically. Consequently, malware has made them its target. So far, although the research and the developed applications for malware detection by Android application developers have made a partial contribution to the solution of the problem, they have been insufficient to completely eliminate this problem due to some of the characteristics and behavioral features of malware. In this study, a dynamic model that combines the outputs of ML-based methods through FIS was proposed for Android malware detection. Two thousand application instances in the form of APK files were employed in the study, one thousand of which were malicious applications downloaded from the Drebin database, and one thousand of which were benign applications downloaded from the Google Play Store. The APK files were first analyzed by reverse engineering, and the Manifest.xml file was obtained. Then, the permissions, intentions and activities contained in this file were determined. After that, by querying each APK file, the data vector of 2000 \(\times\) 1134 consisting of 1's and 0's was saved to the csv file to be used as a dataset in malware detection. The experimental results of the proposed model were obtained by applying this dataset to the model whose feature extraction, feature selection, data splitting, multi-classification, voting and routing and combining scores processes were built using the necessary components in the Azure ML environment. The accuracy of this model was also evaluated against the ML-based models, including the SVM, LR, BPM, BDT, DF and NN methods built in this environment. The classification error rates including the MCR, FNR and FPR, the confusion matrix metrics including the accuracy, recall, specificity, precision and F-score and the AUC metric obtained from ROC curves were used for the assessment of the models. The experiments performed in the random split ratio and five-fold cross validation conditions showed that the proposed FL-BDE model outperformed the ML-based models in terms of both the classification error rates and the confusion matrix metrics. With performance differences of 0.5% or more for the 0.60 split conditions and 0.7% or more for the 0.70 split conditions, the proposed model outperformed the ML-based models in terms of the classification error rates. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Author** & **Method(s)** & **Dataset** & **Acc (\%)** & **Prec (\%)** & **Rec (\%)** & **F-m (\%)** & **AUC (\%)** \\ \hline
8 & [PERSON] et al. [32] & Hybrid approach based on CNN and ANFIS &
\begin{tabular}{c} [PERSON] and \\ CICMalDroid \\ \end{tabular} & 94.67 & 94.78 & 94.67 & 94.66 & 94.87 \\ \hline
9 & FL-BDE model & \
\begin{tabular}{c} Mamdani type-Fuzzy \\ Inference \\ System-based \\ Ensemble approach \\ \end{tabular} &
\begin{tabular}{c} [PERSON] and \\ Google Play \\ Store \\ \end{tabular} & 99.33 & 98.68 & 100.00 & 99.34 & 99.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._of the ROC curves. When the proposed model was compared with similar ensemble-based current literature studies, it was observed that it performed better, with a smaller difference. In comparison with other studies in the literature that produced performance results that are similar to those of our model, it was seen that it had a much better performance in terms of all of the metrics. In the future, real-time malicious detection applications can be realized by creating a hybrid model that obtains the feature vectors from APK application files with DL-based approaches, and then implementing the malicious application detection process by using the FL-BDE approach we propose here. This research received no external funding. The data presented here are available in the article. The author would like to thank Gazi University Academic Writing Application and Research Center for proofreading the article.