Android Malware Classification with Gray Wolf Optimization Algorithm and Deep Neural Network Hybrid Approach

With the rapid development of technology and the increase in the use of Android software, the number of malware has also increased. This study presents a classification as malware/goodware with the features of 4465 Android applications. Cost is an important problem for the increasing number of applications and the analyzes to be made on each application. This study focused on this problem with the hybrid use of Gray Wolf Optimization Algorithm (GWO) and Deep Neural Networks (DNN). With the use of GWO, both feature selection and the features of the model to be created with DNN are determined. In this way, an approximate solution proposal is presented for the most suitable features and the most suitable model design. The model, which was created with the use of GWO-DNN hybrid in this study, offers an F1 score of 99.74%.