Simplified-Xception: A New Way to Speed Up Malicious Code Classification
Xinshuai Zhu, Famei He, Chang Gao, Yushi Wang, Xuren Wang · 2023
Traditional detection methods need to consume a lot of manpower and material resources, causing great difficulties in the research of malicious code. Aiming at the problem of malicious code family classification, this paper proposes an improved convolutional neural network model based on Xception (Simplified-Xception). The model uses the dataset from the 2015 Kaggle Microsoft Malicious Code Classification Contest. Firstly, the malicious code is transformed into the gray image as the model input, the number of modules of the original model is reduced, and a layer of depth-separable convolution with a step size of 2 is added to enhance the generated gray image. The convolutional network model is improved based on the Xception model. The model in this paper is compared with the CNN model, ResNet50, and Inception-related improvement models. The experimental results show that the accuracy of Simplified- Xception is 98%, which is better than that of other inception related models. Compared with the Xception model, the accuracy of the Simplified -Xception model is increased by 1.3%, and the number of parameters is reduced by half.