关键词: 安卓, 恶意应用检测, 机器学习

Abstract: Android platform incurs mass attacks from hackers recently. The rapid increment of Android malwares results in the problems of privacy leakage and property loss. This paper firstly tests the difference between benign apps and malwares in two resource features, these features are pictures and widgets. Then MalAssassin, a method for detection of Android malware is proposed. It performs static analysis on Android APK files, gathering 68 features in 8 different categories which are permissions, components, API calls, commands, hard-coded IP address and certificates extracted from other approaches along with pictures and widgets proposed by this paper. These features are mapped to vector spaces to build the detection model which will classify an app as benign or malicious. In an evaluation with 53,422 benign apps and 5,671 malwares, MalAssassin achieves the performance of 99.1% precision and recall, while adapting well in different datasets.

Key words: Android, malware detection, machine learning