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RFA: Recursive Feature Addition Algorithm for Machine Learning-Based Malware Classification

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(2), pp.61-68
  • DOI : 10.9708/jksci.2021.26.02.061
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 31, 2020
  • Accepted : January 28, 2021
  • Published : February 26, 2021

Ji-Yun Byeon 1 Dae-Ho Kim 1 Hee-Chul Kim 1 Sang-Yong Choi 1

1영남이공대학교

Accredited

ABSTRACT

Recently, various technologies that use machine learning to classify malicious code have been studied. In order to enhance the effectiveness of machine learning, it is most important to extract properties to identify malicious codes and normal binaries. In this paper, we propose a feature extraction method for use in machine learning using recursive methods. The proposed method selects the final feature using recursive methods for individual features to maximize the performance of machine learning. In detail, we use the method of extracting the best performing features among individual feature at each stage, and then combining the extracted features. We extract features with the proposed method and apply them to machine learning algorithms such as Decision Tree, SVM, Random Forest, and KNN, to validate that machine learning performance improves as the steps continue.

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