基于多裁剪的恶意软件检测和分类模型OA北大核心CSTPCD

To solve the problems of missing local key features,insufficient samples,unbalanced samples and low classification accuracy in malware detection and classification,a model named MadcuG based on multi-crop strategy was proposed.Firstly,the malware byte file was put into the memory buffer as a byte array to generate a color image.Secondly,the multi-crop strategy was used to generate local malware images using color images to increase the attention of local key features and solve the problem of sample imbalance and local key feature loss.Finally,the deep convolution generative adversarial network was used to construct two discriminators:the scoring discriminator and the classification discriminator.The objective function was used to calculate the adversarial loss of the scoring discriminator and the generator and the classification loss of the classification discriminator,in order to increase the utilization rate of parameters and the generalization of the model.The experimental results show that the MadcuG model can reach 99.88％and 99.2％on the BIG2015 and Malimg data sets,respectively,which outperforms existing models.