Research on Malware Code Classification Based on Features Fusion
摘要
The features extracted by the static analysis method based on feature code matching often lag behind the development of the virus and cannot detect unknown viruses.Therefore,the feature extraction and fusion are carried out from the virus decompilation file and theirs grayscale image.The Random Forest(RF) algorithm in machine learning is used to classify the malware code family,extract the two local features of the opcode instructions and grayscale texture of the malware code,and use the color histogram as the global feature of the malware code.Experimental results show that the fusion of malware code features and RF algorithm can achieve effective classification of malware code families,and the average accuracy rate is 99.59%.
引用本文（GB/T 7714）
ZHANG Jinglian, PENG Yanbing. Research on Malware Code Classification Based on Features Fusion[J]. DOAJ (DOAJ: Directory of Open Access Journals), 2019.
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