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

The extensive research in machine learning based Android malware detection showcases high-performance metrics through a wide range of proposed solutions. Consequently, this fosters the (mis)conception of being a solved problem, diminishing its appeal for further 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 solved problem. 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.