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Malicious applications (particularly those targeting the Android platform)pose a serious threat to developers and end-users. Numerous research effortshave been devoted to developing effective approaches to defend against Androidmalware. However, given the explosive growth of Android malware and thecontinuous advancement of malicious evasion technologies like obfuscation andreflection, Android malware defense approaches based on manual rules ortraditional machine learning may not be effective. In recent years, a dominantresearch field called deep learning (DL), which provides a powerful featureabstraction ability, has demonstrated a compelling and promising performance ina variety of areas, like natural language processing and computer vision. Tothis end, employing deep learning techniques to thwart Android malware attackshas recently garnered considerable research attention. Yet, no systematicliterature review focusing on deep learning approaches for Android Malwaredefenses exists. In this paper, we conducted a systematic literature review tosearch and analyze how deep learning approaches have been applied in thecontext of malware defenses in the Android environment. As a result, a total of132 studies covering the period 2014-2021 were identified. Our investigationreveals that, while the majority of these sources mainly consider DL-based onAndroid malware detection, 53 primary studies (40.1 percent) design defenseapproaches based on other scenarios. This review also discusses researchtrends, research focuses, challenges, and future research directions inDL-based Android malware defenses.

Deep Learning for Android Malware Defenses: a Systematic Literature Review