关键词: 恶意软件检测, 对抗样本, 检测逃逸, 卷积注意力机制, 生成对抗网络

Abstract: In the context of artificial intelligence, an increasing number of machine learning algorithms are being applied in the field of malicious software detection. However, a significant challenge in practical scenarios is the imbalance in data, where the quantity of malicious software is notably lower than benign software. Addressing this issue, we propose a novel generative adversarial network(GAN) detection escape model, incorporating a convolutional attention mechanism. This model is capable of generating adversarial samples of malicious software that can evade detection by the classifier. Experimental comparisons were conducted to evaluate the performance of this escape model, along with escape models based on deep neural networks and convolutional neural networks, across seven different malicious software classifiers. The results demonstrate that this escape model can achieve higher evasion rates without explicit knowledge of the internal structure of the detection model, offering a new perspective for generating highquality adversarial samples.

Key words: malware detection, adversarial sample, detection of evasion, convolutional attention mechanism, generative adversarial network