Analysis of Android malware detection performance using machine learning classifiers

As mobile devices have supported various services and contents, much personal information such as private SMS messages, bank account information, etc. is scattered in mobile devices. Thus, attackers extend the attack range not only to the existing environment of PC and Internet, but also to the mobile device. Previous studies evaluated the malware detection performance of machine learning classifiers through collecting and analyzing event, system call, and log information generated in Android mobile devices. However, monitoring of unnecessary features without understanding Android architecture and malware characteristics generates resource consumption overhead of Android devices and low ratio of malware detection. In this paper, we propose new feature sets which solve the problem of previous studies in mobile malware detection and analyze the malware detection performance of machine learning classifiers.