In order to identify and analyze malware efficiently and prevent possible harm in time, a static classification method based on image gray texture features is proposed. According to the instruction length characteristics of the code, multi byte image texture of virus code is designed and extracted, and unified into two-dimensional features. Then all feature files are used as training sets for random forest machine learning method classification. The experiment using standard data sets shows that the method can achieve 96.36% accuracy.
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