  Since the threat of malicious software (malware) has become increasingly
serious, automatic malware detection techniques have received increasing
attention, where machine learning (ML)-based visualization detection methods
become more and more popular. In this paper, we demonstrate that the
state-of-the-art ML-based visualization detection methods are vulnerable to
Adversarial Example (AE) attacks. We develop a novel Adversarial Texture
Malware Perturbation Attack (ATMPA) method based on the gradient descent and
L-norm optimization method, where attackers can introduce some tiny
perturbations on the transformed dataset such that ML-based malware detection
methods will completely fail. The experimental results on the MS BIG malware
dataset show that a small interference can reduce the accuracy rate down to 0%
for several ML-based detection methods, and the rate of transferability is
74.1% on average.
