Not All Malware are Born Equally: An Empirical Analysis of Adversarial Evasion Attacks in Relation to Malware Types and PE Files Structure

Malware white-box evasion attack is a serious threat to machine learning-based malware classification models, where an attacker carefully inserts perturbations into a malware executable at a test time to evade a target model. Previous research introduced different white-box evasion attacks, namely padding and slack attacks, to craft malware adversarial samples and evaluated them based on the perturbation size and their evasion rate against a target model. However, there is a lack of insights into how the malware file structure and type affect the adversarial malware sample generation and their respective evasion rate. In this work, we provide a comprehensive empirical analysis by factoring in the malware structure and the type. Our analysis quantifies slack space availability in various sections, exploring how the slack space can influence the robustness of detection techniques. We further assess the relationship between malware type and evasion rate to understand how different types of malware respond to evasion attacks. Additionally, we explore the connection between each malware type and the corresponding slack space availability, analyzing how these structural factors influence the evasion rates during adversarial attacks. In our experiments, adversarial malware samples were generated using two different algorithms: gradient descent and iterative gradient sign method. This detailed analysis enhances our understanding of evasion dynamics of adversarial attacks across malware types and different structural characteristics of binary malware files.