The traditional malware classification method relies too much on expert extraction features, and the malware image visualization method contains fewer features. To deal with these problems, we propose a multi-channel ...
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ISBN:
(纸本)9781728127767
The traditional malware classification method relies too much on expert extraction features, and the malware image visualization method contains fewer features. To deal with these problems, we propose a multi-channel visualization method for malware classification based on deep learning. Firstly, the malware binary file is divided into a 256x256-dimensional matrix according to the width of 256 bytes. Secondly, the Word2Vec algorithm is used to calculate the 256-dimensional vector of each byte in each binary file, and then the file is converted to a 256x256-dimensional matrix. Thirdly, we use the Word2Vec algorithm to calculate the 256-dimensional vector of each assembly instruction in each assembly file, and then the file is converted into a 256x256-dimensional matrix. Fourthly, for each malware sample, 3 matrixes are combined into an uncompressed multi-channel image. Finally, the LeNetS is used for training classification model. The experimental results show that the average accuracy is 98.76%.
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