Familial Classification of Android Malware using Hybrid Analysis

With the developments in mobile and wireless technology, mobile devices have become important part of our lives. While Android is the leading operating system in the market share, it is also the most targeted platform by attackers. While there have been many solutions proposed for detection of Android malware in the literature, the family classification of detected malicious applications becomes important, especially where the number of mobile malware variants increases every day in the market. In this study, a solution based on machine learning and hybrid analysis is proposed for the Android malware familial classification problem. An extensive feature set including network-related features and activity bigrams is proposed. The effective static and dynamic analysis features are studied thoroughly and evaluated on Malgenome [1], Drebin [2], and UpDroid [3] datasets.