  Malicious applications (particularly those targeting the Android platform)
pose a serious threat to developers and end-users. Numerous research efforts
have been devoted to developing effective approaches to defend against Android
malware. However, given the explosive growth of Android malware and the
continuous advancement of malicious evasion technologies like obfuscation and
reflection, Android malware defense approaches based on manual rules or
traditional machine learning may not be effective. In recent years, a dominant
research field called deep learning (DL), which provides a powerful feature
abstraction ability, has demonstrated a compelling and promising performance in
a variety of areas, like natural language processing and computer vision. To
this end, employing deep learning techniques to thwart Android malware attacks
has recently garnered considerable research attention. Yet, no systematic
literature review focusing on deep learning approaches for Android Malware
defenses exists. In this paper, we conducted a systematic literature review to
search and analyze how deep learning approaches have been applied in the
context of malware defenses in the Android environment. As a result, a total of
132 studies covering the period 2014-2021 were identified. Our investigation
reveals that, while the majority of these sources mainly consider DL-based on
Android malware detection, 53 primary studies (40.1 percent) design defense
approaches based on other scenarios. This review also discusses research
trends, research focuses, challenges, and future research directions in
DL-based Android malware defenses.
