Abstract:

With the wide spread of Android-based mobile applications, the problem of information security in Android system is increasingly serious . Although Android operating system adopted independent virtual memory space to guarantee the reliability of its kernel , because of calls and association between various events in application , it will lead to private data leakage , unauthorized operation procedures, attacks to run out the battery , malicious processes interact and other mobile security events. Therefore , Android malware detection techniques become a hot topic in the domain of mobile application security. In this paper, the application requirements and environments for Android malware detection are firstly described, and then the diversity malware detection methods are surveyed which include dynamic and static methods, machine learning-based schemes, formal method-based software engineering techniques. Finally, the research direction to initiate a comprehensive static detection framework by integrating machine learning and software engineering is proposed, with some key challenges concomitantly analyzed, which can be valuable reference both for academic communities and industrial products.

Key words: mobile malware, dynamic detection, static detection, machine learning, model checking