Review of Deep Learning-based Malware Detection for Android and Windows System

TL;DR

This paper reviews AI-enabled malware detection techniques for Android and Windows, highlighting their effectiveness in achieving perfect accuracy against obfuscation tactics used by modern malware.

Contribution

It provides a comparative review of two AI-based malware detection methods for Android and Windows, emphasizing their robustness and high accuracy.

Findings

Both techniques achieved perfect detection accuracy.

AI-enabled systems are more robust against obfuscation.

The review underscores the importance of AI in malware detection.

Abstract

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.