Android Malware Detection Using Static Features and Machine Learning

Abstract

Smartphones are prone to cyber-attacks using malware applications, this can compromise the security of the phone thus affecting the privacy of any personal or financial information. Machine learning has proven to work in various fields including security. In this paper we propose a machine learning for android malware detection where the main focus is to use various static features of Android Application Package (APK). Features such as permissions, API calls, services, opcodes, and activities to train different machine learning models to classify an APK file as malware or benign. We found among the experimented machine learning models, we found that the Gaussian Process showed the most promising results followed by Random Forest and Decision Trees.