Malicious code attacks have severely hindered the current development of the Internet technologies. Once the devices are infected with virus, the damages to companies and users are unpredictable. Although researchers have developed malware detection methods, the analysis result still cannot achieve the desired accuracy due to complicated malicious code families and fast-growing variants. In this paper, to solve this problem, we combine Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to design an efficient and accurate malware detection method. First, we implement a code visualization method and utilize GAN to generate more samples of malicious code variants in the role of data augmentation. Then, the lightweight AlexNet originated from CNN to classify malware families. Finally, simulation experiments are conducted to evaluate that our CNN plus GAN model can achieve a higher classification accuracy (i.e., 97.78%) compared with some related work.

中文翻译：

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基于 CNN 和 GAN 的恶意代码家族分类：一种代码可视化方法

恶意代码攻击已经严重阻碍了当前互联网技术的发展。一旦设备感染病毒，对企业和用户造成的损失是不可预测的。尽管研究人员开发了恶意软件检测方法，但由于恶意代码家族复杂、变种快速增长，分析结果仍然无法达到预期的准确性。在本文中，为了解决这个问题，我们将卷积神经网络 (CNN) 与生成对抗网络 (GAN) 相结合，设计了一种高效准确的恶意软件检测方法。首先，我们实现了一种代码可视化方法，并利用 GAN 在数据增强的作用下生成更多的恶意代码变体样本。然后，起源于 CNN 的轻量级 AlexNet 用于对恶意软件家族进行分类。最后，