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Home > Archive>Volume 31, Issue 5, 2020 >1454-1464. DOI:10.13328/j.cnki.jos.005660
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Malicious Code Classification Method Based on Deep Forest
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TP311

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Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University (ZZ2018020); National Program on Key Basic Research Project of China (973) (2013CB329104); Fund of State Key Laboratory of Science and Technology on Information Transmission and Dissemination in Communication Networks

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    Abstract:

    Aiming at the problem of insufficient accuracy of current static classification method of malicious code, this study maps the malicious code into uncompressed gray-scale image. Then the image is transformed into a constant-size image according to the image transformation method, and the direction gradient histogram is used to extract the features of the image. Finally, a kind of malicious code classification method based on deep forest is proposed. Experiments on malicious code samples from different families verify the effectiveness of the proposed method and the results are superior to the recently proposed SPAM-GIST method.

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卢喜东,段哲民,钱叶魁,周巍.一种基于深度森林的恶意代码分类方法.软件学报,2020,31(5):1454-1464

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History
  • Received:December 01,2017
  • Revised:June 01,2018
  • Online: May 18,2020
  • Published: May 06,2020
Links:Institute of Software, CASChina Computer FederationCASNSFC
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