  Malicious applications (especially in the Android platform) are a serious
threat to developers and end-users. Many research efforts have hence been
devoted to developing effective approaches to defend Android malware. However,
with the explosive growth of Android malware and the continuous advancement of
malicious evasion technologies like obfuscation and reflection, android malware
defenses based on manual rules or traditional machine learning may not be
effective due to limited apriori knowledge. In recent years, a dominant
research field of deep learning (DL) with the powerful feature abstraction
ability has demonstrated a compelling and promising performance in various
fields, like Nature Language processing and image processing. To this end,
employing deep learning techniques to thwart the attack of Android malware has
recently gained considerable research attention. Yet, there exists no
systematic literature review that focuses on deep learning approaches for
Android Malware defenses. 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 104 studies were identified over the period 2014-2020. The results of
our investigation show that even though most of these studies still mainly
consider DL-based on Android malware detection, 35 primary studies (33.7\%)
design the defenses approaches based on other scenarios. This review also
describes research trends, research focuses, challenges, and future research
directions in DL-based Android malware defenses.
