Review of KIISC (정보보호학회지)

Korea Institute of Information Security and Cryptology (한국정보보호학회)
권호 표지

Analysis of Research Trend on Machine Learning Based Malware Mutant Identification

기계 학습을 활용한 변종 악성코드 식별 연구 동향 분석

  • 유정빈 (연세대학교 정보보호연구실) ;
  • 신민식 (연세대학교 정보보호연구실) ;
  • 권태경 (연세대학교 컴퓨터과학과)
  • Published : 2017.06.30
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Abstract

기하급수적으로 증가하고 있는 변종 악성코드에 대응하기 위한 식별 연구가 다양화 되고 있다. 최근 연구에서는 기존 악성코드 분석 기술 (정적/동적)의 개별 사용 한계를 파악하고, 각 방식을 혼합한 하이브리드 분석으로 전환하는 추세이다. 나아가 변종 식별이 어려운 악성코드를 더욱 정확하게 식별하기 위해 기계 학습을 적용하기에 이르렀다. 이에 따라, 본 논문에서는 변종 악성코드 식별을 위해 각 연구에서 활용한 기계 학습 기술과 사용한 악성코드 특징을 중심으로 변종 악성코드 식별 연구를 분류 및 분석한다.

Keywords

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