Deep Learning based Malware Detection for Android Systems: A Comparative Analysis

Abstract

Nowadays, cyber attackers focus on Android, which is the most popular open-source operating system, as main target by applying some malicious software (malware) to access users' private information, control the device, or harm end-users. To detect Android malware, security experts have offered some learning-based models. In this study, we developed an Android malware detection system that uses different machine\deep learning models by performing both dynamic analyses, in which suspected malware is executed in a safe environment for observing its behaviours, and static analysis, which examines a malware file without any execution on the Android device. The benefits and weaknesses of these models and analyses are described in detail in this comparative study, and directions for future studies are drawn. Experimental results showed that the proposed models gave better results than those in the literature, with 0.988 accuracy for LSTM on static analysis and 0.953 accuracy for CNN-LSTM on dynamic analysis.