Machine Learning for Android Malware Detection: Mission Accomplished? A Comprehensive Review of Open Challenges and Future Perspectives

The vast body of machine learning based Android malware detection research, reporting high-performance metrics using a wide variety of proposed solutions, enables the logical derivation of the (mis)conception of being a problem solved and, therefore, losing its appeal as a field of research. However, after surveying and scrutinizing the related literature, this deceptive deduction is debunked. In this paper, we identify five significant unresolved challenges neglected by the specialized research that prevent the qualification of Android malware detection as a problem solved. From methodological flaws to invalid postulates and data set limitations, these challenges, which are thoroughly described throughout the paper, hamper effective, long-term machine learning based Android malware detection. This comprehensive review of the state-of-the-art highlights and motivates future research directions in the Android malware detection domain that may bring the problem closer to being solved.