A Review on The Use of Deep Learning in Android Malware Detection
It addresses the problem of Android malware detection for researchers and practitioners by summarizing existing work, but it is incremental as a review paper.
This paper reviews the use of deep learning methods in Android malware detection over the past five years, covering static, dynamic, and hybrid analysis approaches to identify progress and unresolved issues.
Android is the predominant mobile operating system for the past few years. The prevalence of devices that can be powered by Android magnetized not merely application developers but also malware developers with criminal intention to design and spread malicious applications that can affect the normal work of Android phones and tablets, steal personal information and credential data, or even worse lock the phone and ask for ransom. Researchers persistently devise countermeasures strategies to fight back malware. One of these strategies applied in the past five years is the use of deep learning methods in Android malware detection. This necessitates a review to inspect the accomplished work in order to know where the endeavors have been established, identify unresolved problems, and motivate future research directions. In this work, an extensive survey of static analysis, dynamic analysis, and hybrid analysis that utilized deep learning methods are reviewed with an elaborated discussion on their key concepts, contributions, and limitations.