Malware Variant Detection Based on Decomposed Deep Convolutional Network
Jianbin Mai, Chunjie Cao, Shi Fangfei, Xiaoqing Chen · 2021
Aiming at the existing malware variants detection method based on deep convolutional neural networks (DCNN) has the problem of large computational resource consumption, a decomposing deep neural network (Dec-DCNN) is proposed for optimization. The computational cost was reduced by using single value decomposition (SVD) to split the pre-trained standard convolution operation into two simpler convolution operations. And the optimized network after decomposition does no need to be retrained, which can reduce the number of parameters and calculations while maintaining the detection accuracy of the pre-trained model. Experimental results show that the Dec-DCNN had a detection time of only 46% of DCNN with an overall detection accuracy of 98.5%. At the same time, compared with the current mainstream malicious variant detection model, Dec-DCNN has a stronger ability to express texture features of malware images.