  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.
