Review of Deep Learning-based Malware Detection for Android and Windows System
This is an incremental review summarizing existing methods for improving malware detection in specific operating systems.
This paper reviews deep learning-based malware detection techniques for Android and Windows systems, addressing the challenge of AI-enabled malware that evades traditional methods, and reports that both techniques achieved perfect accuracy in detecting various malware families.
Differentiating malware is important to determine their behaviors and level of threat; as well as to devise defensive strategy against them. In response, various anti-malware systems have been developed to distinguish between different malwares. However, most of the recent malware families are Artificial Intelligence (AI) enable and can deceive traditional anti-malware systems using different obfuscation techniques. Therefore, only AI-enabled anti-malware system is robust against these techniques and can detect different features in the malware files that aid in malicious activities. In this study we review two AI-enabled techniques for detecting malware in Windows and Android operating system, respectively. Both the techniques achieved perfect accuracy in detecting various malware families.