CNN- and GAN-based classification of malicious code families: A code visualization approach

Abstract

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.