## 2 Literature Review
The researchers reported that the proposed model achieved an accuracy of 97.53%, a precision of 96.66%, a recall of 98.41% and an F1-Score of 97.53%, reflecting the result of the evaluations using the AMGP and Drebin datasets. [PERSON] et al. [27] transformed the process of generating rules for malware detection into a two-fold optimization problem. As a result, their proposed Two-Level Malicious Application Detection (BMD) model had an F-measure of 97.79% and an accuracy of 98.18%. With the method that they called DEEPSEL, [PERSON] et al. [28] evaluated whether the application was malicious or not by evaluating several features of the dataset. The name of the model refers to the deep feature selection process they utilized in the process. In this study, machine learning-based algorithms and deep learning (DL)-based models were combined to perform the classification, and Particle Swarm Optimization was utilized for the feature selection. As a result of the evaluation performed on the CICAndMal2017 [29] dataset, the proposed model obtained 83.6% accuracy, 82.4% precision, 82.5% recall and an 82.5% F-measure. [PERSON] et al. [17] detected malware by training two artificial neural networks, which they built using two-dimensional API images. These images serve as a signature of a program's activities over time. Subsequently, the features with the biggest impact on the findings were selected from the given features using an autoencoder and conventional artificial neural networks. The accuracy, precision and F-measure metrics for this model, which consists of two encoders and a SoftMax artificial neural network, were 94%, 98% and 97%, respectively. [PERSON] et al. [30] developed a novel fuzzy integral-based multi-classifiers ensemble model for Android malware detection. They combined the results of the XGBoost, RF, DT, AdaBoost and Light-GBM classifiers over the Choquet fuzzy integral. The experimental results obtained through the dataset, consisting of 9476 benevolent and 5560 malicious applications, showed that their proposed approach based on the Choquet fuzzy integral technique achieved a higher performance, with an accuracy value of 95.08% compared to those of the classifiers that were used individually. The risk-based fuzzy analytical hierarchy process approach was applied by [PERSON] et al. [18] in their Multi-Criteria Based Decision System and Mobile Malware Detection system. This methodology, which involved a static analysis, evaluated the permission-based features with the purpose of increasing the user's awareness of high-risk permissions through a risk analysis. The accuracy value for evaluations on the Drebin and AndroZoo datasets was 90.54%. [PERSON] and [PERSON] [31] built a Multifaceted Deep Generative Adversarial Networks (MDGAN) model for effective malware detection. With the three-stage proposed model, they converted the APK files into a binary image and an API sequence in the first stage. Then, by sending the image files to GoogleNet and sending the API sequence to the LSTM network, they obtained and determined the distinctive and stable features, respectively. Then, they applied the data with the combined feature to the Generative Adversarial Networks (GAN) and determined whether the data were a malicious application. They used the AndroZoo and Drebin databases as the dataset. They proved that the proposed model, with an accuracy value of 96.2% and an F-score value of 94.7%, which were obtained from the experimental studies, outperforms the studies that have been conducted in this context recently. In a different study, [PERSON] et al. [32] used permission-based analysis and created a hybrid model by fusing CNN with FL. In the study, they used two different datasets and 500 benign and malicious applications. They analyzed the APK file of the applications and acquired a manifest.xml file. In the next stage, they obtained the permission information of the applications from this file.