Abstract: With the development of Internet, malicious code tend to be massive and polymorphic. The classification of malicious code family is one of the challenges of cyber security. Combining the semi supervised generation network with the deep convolutional neural network, a multi-family malicious code classification model is proposed. Taking the gray image of malicious codes as the feature, based on the efficient one-dimensional convolutional neural network (1D-CNN), using the semi-supervised generative adversarial network (SGAN), an efficient and accurate malicious code family classification model is constructed as SGAN-CNN, which can improve the malicious code classification ability from aspects of efficient feature extraction and SGAN optimization. In order to verify the classification ability of the model, experiments are carried out on the Microsoft malware classification challenge data set. 5-fold cross-validation shows that the proposed model achieves 98.81% of the average accuracy of the test set with 80% of the tag rate, 98.01% of the average accuracy of the test set with 20% of the tag rate, and achieves better experimental results. In the case of small samples, it can also achieve good classification accuracy.

Key words: deep learning, one-dimensional convolutional neural network, semi-supervised learning, generative adversarial network, malicious code classification