Deep learning technology has made great achievements in the field of image.
In order to defend against malware attacks, researchers have proposed many
Windows malware detection models based on deep learning. However, deep learning
models are vulnerable to adversarial example attacks. Malware can generate
adversarial malware with the same malicious function to attack the malware
detection model and evade detection of the model. Currently, many adversarial
defense studies have been proposed, but existing adversarial defense studies
are based on image sample and cannot be directly applied to malware sample.
Therefore, this paper proposes an adversarial malware defense method based on
adversarial training. This method uses preprocessing to defend simple
adversarial examples to reduce the difficulty of adversarial training.
Moreover, this method improves the adversarial defense capability of the model
through adversarial training. We experimented with three attack methods in two
sets of datasets, and the results show that the method in this paper can
improve the adversarial defense capability of the model without reducing the
accuracy of the model.

本文提出了一种基于对抗训练的对抗恶意软件防御方法，通过对预处理的简单对抗样本进行防御，提高了模型的对抗防御能力，在两组数据集的三种攻击方法中表现良好，且不影响模型准确性。