Attacks on Visualization-Based Malware Detection: Balancing Effectiveness and Executability

TL;DR

This paper reviews visualization-based malware detection methods, analyzes their vulnerabilities to adversarial attacks, and introduces a new attack that effectively evades detection while preserving malware functionality, achieving a 98% success rate.

Contribution

It presents a novel adversarial attack on visualization-based malware detection that overcomes previous limitations and maintains malware executability.

Findings

Achieved 98% success rate in evading detection

Identified limitations of existing attacks and defenses

Proposed a new attack method that preserves malware functionality

Abstract

With the rapid development of machine learning for image classification, researchers have found new applications of visualization techniques in malware detection. By converting binary code into images, researchers have shown satisfactory results in applying machine learning to extract features that are difficult to discover manually. Such visualization-based malware detection methods can capture malware patterns from many different malware families and improve malware detection speed. On the other hand, recent research has also shown adversarial attacks against such visualization-based malware detection. Attackers can generate adversarial examples by perturbing the malware binary in non-reachable regions, such as padding at the end of the binary. Alternatively, attackers can perturb the malware image embedding and then verify the executability of the malware post-transformation. One major limitation of the first attack scenario is that a simple pre-processing step can remove the perturbations before classification. For the second attack scenario, it is hard to maintain the original malware's executability and functionality. In this work, we provide literature review on existing malware visualization techniques and attacks against them. We summarize the limitation of the previous work, and design a new adversarial example attack against visualization-based malware detection that can evade pre-processing filtering and maintain the original malware functionality. We test our attack on a public malware dataset and achieve a 98% success rate.