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

Xinbo Liu; Jiliang Zhang; Yaping Lin; He Li

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arXiv · 1808.01546

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

Xinbo Liu·Jiliang Zhang·Yaping Lin·He Li

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Abstract

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.

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BibTeX RIS

Xinbo Liu, Jiliang Zhang, Yaping Lin, He Li. 2018-08-05. ATMPA: Attacking Machine Learning-based Malware Visualization Detection Methods via Adversarial Examples. https://arxiv.org/abs/1808.01546

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