ATMPA：基于攻击机器学习的恶意软件可视化检测方法
ATMPA: Attacking Machine Learning-based Malware Visualization Detection Methods via Adversarial Examples
摘要：
随着恶意软件（malware）威胁的日益严重，恶意软件自动检测技术受到越来越多的关注，其中基于机器学习（ML）的可视化检测方法越来越流行。在本文中，我们证明了最先进的基于ML的可视化检测方法容易受到对抗性示例（AE）攻击。基于梯度下降和L-范数优化方法，提出了一种新的对抗性纹理恶意软件扰动攻击（ATMPA）方法，攻击者可以在变换后的数据集上引入一些微小的扰动，使得基于ML的恶意软件检测方法完全失效。在MS BIG恶意软件数据集上的实验结果表明，对于几种基于ML的检测方法，小的干扰可以将准确率降低到0%，并且平均可转移率为74.1%。
英文摘要：
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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