Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Malicious Traffic Detection Method Using X-means Clustering

X-means 클러스터링을 이용한 악성 트래픽 탐지 방법

  • 한명지 (서울대학교 컴퓨터공학부) ;
  • 임지혁 (서울대학교 컴퓨터공학부) ;
  • 최준용 (서울대학교 컴퓨터공학부) ;
  • 김현준 (서울대학교 컴퓨터공학부) ;
  • 서정주 (서울대학교 컴퓨터공학부) ;
  • 유철 (서울대학교 컴퓨터공학부) ;
  • 김성렬 (건국대학교 인터넷미디어공학부) ;
  • 박근수 (서울대학교 컴퓨터공학부)
  • Received : 2014.02.28
  • Accepted : 2014.05.30
  • Published : 2014.09.15
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Abstract

Malicious traffic, such as DDoS attack and botnet communications, refers to traffic that is generated for the purpose of disturbing internet networks or harming certain networks, servers, or hosts. As malicious traffic has been constantly evolving in terms of both quality and quantity, there have been many researches fighting against it. In this paper, we propose an effective malicious traffic detection method that exploits the X-means clustering algorithm. We also suggest how to analyze statistical characteristics of malicious traffic and to define metrics that are used when clustering. Finally, we verify effectiveness of our method by experiments with two released traffic data.

악성 트래픽은 디도스 공격, 봇넷 통신 등의 인터넷 망을 교란시키거나 특정 네트워크, 서버, 혹은 호스트에 피해를 끼칠 의도를 가지고 발생시키는 트래픽을 지칭한다. 이와 같은 악성 트래픽은 인터넷이 발생한 이래 꾸준히 양과 질에서 진화하고 있고 이에 대한 대응 연구도 계속되고 있다. 이 논문에서는 악성 트래픽을 기존 X-means 클러스터링 알고리즘을 적용하여 효과적으로 탐지하는 방법을 제시하였다. 특히 악성 트래픽의 통계적 특징을 분석하고 클러스터링을 위한 메트릭을 정의하는 방법을 체계적으로 제시하였다. 또한 두 개의 공개된 트래픽 데이터에 대한 실험을 통해 실효성을 검증하였다.

Keywords

Acknowledgement

Supported by : 한국연구재단

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