Homology analysis of malware based on graph

Homology analysis of malware based on graph

Blog Article

Malware detection and homology analysis has been the hotspot of malware analysis.API call graph of malware can represent the behavior of it.Because of the subgraph isomorphism algorithm has high complexity,the analysis of malware based on the graph read more structure with low efficiency.

Therefore,this studies a homology analysis method of API borstlist självhäftande graph of malware that use convolutional neural network.By selecting the key nodes,and construct neighborhood receptive field,the convolution neural network can handle graph structure data.Experimental results on 8 real-world malware family,shows that the accuracy rate of homology malware analysis achieves 93%,and the accuracy rate of the detection of malicious code to 96%.