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Malware Classification Using Machine Learning

  • JinSu Kang
  • Yoojae WonEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 536)

Abstract

Recently, tools for generating malware have been spreading rapidly on the internet, making it easier for people without expertise to create malware. As a result, the number of malware variants is increasing quickly. To address this issue, it is crucial to classify malware quickly and accurately. However, malware variants are evolving to evade traditional malware-detecting methods based on signature pattern matching. To solve this problem, researches on detection of malware have been made in various fields. In the present study, we first propose a classification method to extract feature data from malware files that is applicable to machine learning, and then we classify malware through learning. Finally, we apply our classification method to sample data to evaluate performance and analyze the results.

Keywords

Computer security Metamorphic Polymorphic Malware classification Machine learning 

Notes

Acknowledgments

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2016-0-00304) supervised by the IITP (Institute for Information & communications Technology Promotion).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science EngineeringChungnam National UniversityDaejeonSouth Korea

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