Deep Learning for Android Malware Defenses: a Systematic Literature Review

Yue Liu1; Chakkrit Tantithamthavorn1; Li Li1; Yepang Liu2

2022

10.1145/3544968

ACM Computing Surveys   影响因子和分区

0360-0300

1557-7341

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 DL techniques to thwart Android malware attacks has recently garnered considerable research attention. Yet, no systematic literature review focusing on DL approaches for Android malware defenses exists. In this article, we conducted a systematic literature review to search and analyze how DL 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 Android malware detection, 53 primary studies (40.1%) 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.

Android malware defenses malware analysis malware detection deep learning reviews mobile security

SCI

英语

其他

Computer Science

Computer Science, Theory & Methods

WOS:000905475300001

ASSOC COMPUTING MACHINERY

COMPUTER SCIENCE

人工提交

1.Monash University
2.Southern University of Science and Technology

Yue Liu,Chakkrit Tantithamthavorn,Li Li,et al. Deep Learning for Android Malware Defenses: a Systematic Literature Review[J]. ACM Computing Surveys,2022,55(8):1-36.

Yue Liu,Chakkrit Tantithamthavorn,Li Li,&Yepang Liu.(2022).Deep Learning for Android Malware Defenses: a Systematic Literature Review.ACM Computing Surveys,55(8),1-36.

Yue Liu,et al."Deep Learning for Android Malware Defenses: a Systematic Literature Review".ACM Computing Surveys 55.8(2022):1-36.