Review of Deep Learning-based Malware Detection for Android and Windows System
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.
Keywords
Cite
@article{arxiv.2307.01494,
title = {Review of Deep Learning-based Malware Detection for Android and Windows System},
author = {Nazmul Islam and Seokjoo Shin},
journal= {arXiv preprint arXiv:2307.01494},
year = {2023}
}
Comments
Presented at the 33rd Joint Conference on Communications and Information (JCCI 2023)