基于卷积注意力机制的恶意软件样本增强方案OA北大核心CSTPCD

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.