A Survey on Adversarial Attacks for Malware Analysis
TL;DR
This survey examines adversarial attacks that deceive ML-based malware classifiers through minor perturbations, highlighting vulnerabilities in critical infrastructure defenses. It aims to provide a comprehensive overview of these threats and their implications.
A Survey on Adversarial Attacks for Malware Analysis
Kshitiz Aryal; Maanak Gupta; Mahmoud Abdelsalam; Pradip Kunwar; Bhavani Thuraisingham
https://doi.org/10.1109/ACCESS.2024.3519524
Volume 13
Machine learning-based malware analysis approaches are widely researched and deployed in critical infrastructures for detecting and classifying evasive and growing malware threats. However, minor perturbations or ineffectual byte insertions can easily ‘fool’ these trained ML classifiers, making them ineffective against these crafted and smart malicious software. This survey aims to provide an ency...