Abstract:

Malware is one of the main threats to Internet security at present. This paper took the rapid and effective detection of malware as the research purpose, proposed SIC model,which used SimHash method to transform malware into feature vector by using the location and quantity characteristics of the opcode of malware,and finally converted it into gray-scale image. Then, the convolutional neural network CNN was used to identify the family of the malware. During this period, this paper used MutiHash and block selection algorithm to optimize the SIC model. The malware classification challenge data set released by Microsoft in 2015 was selected for model training. The experimental results show that the detection and recognition accuracy of the SIC model can reach 96.774%, which is improved to a certain extent compared with other traditional machine learning malware classification methods and achieves good results.

Key words: malware, static analysis, SimHash, CNN