DOI QR코드

DOI QR Code

비정상도메인 분류를 위한 DNS 쿼리 기반의 주성분 분석을 이용한 성분추출

Feature Selection with PCA based on DNS Query for Malicious Domain Classification

  • 투고 : 2012.05.16
  • 심사 : 2012.08.13
  • 발행 : 2012.10.30

초록

최근 봇넷(Botnet)은 탐지 기술을 피하기 위하여 C&C(Command and Control)서버 접속시 DNS(Domain Name System) 서비스를 이용하고 있다. DNS 서비스를 이용한 비정상 행위에 대응하기 위해서 DNS 트래픽 기반의 분석 연구가 필요하다. 본 논문에서는 좀비PC의 C&C서버 도메인주소 질의와 같은 DNS트래픽 기반의 비정상 도메인 분류(Classification)를 위해서 DNS트래픽 수집 및 지도학습(Supervised Learning)에 대해 연구한다. 특히, 본 논문에서는 PCA(Principal Component Analysis) 주성분분석 기술을 통해 DNS 기반의 분류시스템에서의 효과적인 분석 성분들을 구성할 수 있다.

Recent botnets are widely using the DNS services at the connection of C&C server in order to evade botnet's detection. It is necessary to study on DNS analysis in order to counteract anomaly-based technique using the DNS. This paper studies collection of DNS traffic for experimental data and supervised learning for DNS traffic-based malicious domain classification such as query of domain name corresponding to C&C server from zombies. Especially, this paper would aim to determine significant features of DNS-based classification system for malicious domain extraction by the Principal Component Analysis(PCA).

키워드

참고문헌

  1. http://en.wikipedia.org/wiki/Botnet
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피인용 문헌

  1. Detecting Cyber Threats Domains Based on DNS Traffic vol.37B, pp.11, 2012, https://doi.org/10.7840/kics.2012.37B.11.1082