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    Analysis of Feature Importance and Interpretation for Malware Classification

    Dong-Wook Kim1, Gun-Yoon Shin1, Myung-Mook Han2, *

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1891-1904, 2020, DOI:10.32604/cmc.2020.010933

    Abstract This study was conducted to enable prompt classification of malware, which was becoming increasingly sophisticated. To do this, we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were identified. Initially, the analysis features were extracted using Cuckoo Sandbox, an open-source malware analysis tool, then the features were divided into five categories using the extracted information. The 804 extracted features were reduced by 70% after selecting only the most suitable ones for malware classification using a learning model-based feature selection More >

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