Detection of Android Malware using Machine Learning

Nowadays, smartphones became an integral part of human life due to the great necessity for their daily activities. Most smartphone users are downloading and installing mobile apps without worrying about security. Therefore, smartphones are a winning goal for malware, mainly with Android devices. So, it was necessary to provide an intelligent model for detecting malware applications before installing them on android smartphones. In this paper, an intelligent model using machine learning algorithms is proposed for detecting the malware applications in smartphones based on the static malware analysis technique. The contents of an android application features are analyzed, such as permissions, intents, system commands, and API-calls that were extracted from the application manifest file and source code. The proposed model is applied on two separate datasets DREBIN and MALGENOME. The experimental results are compared with some other similar researches, and their results were analyzed. The results indicate that the performance of the proposed model outperforms other similar models for some selected classifiers and can reliably detect both malware and benign Android applications with high accuracy.