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Since the threat of malicious software (malware) has become increasinglyserious, automatic malware detection techniques have received increasingattention, where machine learning (ML)-based visualization detection methodsbecome more and more popular. In this paper, we demonstrate that thestate-of-the-art ML-based visualization detection methods are vulnerable toAdversarial Example (AE) attacks. We develop a novel Adversarial TextureMalware Perturbation Attack (ATMPA) method based on the gradient descent andL-norm optimization method, where attackers can introduce some tinyperturbations on the transformed dataset such that ML-based malware detectionmethods will completely fail. The experimental results on the MS BIG malwaredataset show that a small interference can reduce the accuracy rate down to 0%for several ML-based detection methods, and the rate of transferability is74.1% on average.

ATMPA: Attacking Machine Learning-based Malware Visualization Detection Methods via Adversarial Examples