Journal of KIISE:Information Networking (한국정보과학회논문지:정보통신)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Wireless Internet Service Classification using Data Mining

데이터 마이닝을 이용한 무선 인터넷 서비스 분류기법

  • Published : 2009.06.15
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Abstract

It is a challenging work for service operators to accurately classify different services, which runs on various wireless networks based upon numerous platforms. This works focuses on design and implementation of a classifier, which accurately classifies applications, which are captured horn WiBro Network. Notion of session is introduced for the classifier, instead of commonly used Flow to develop a classifier. Based on session information of given traffic, two classification algorithms are presented, Classification and Regression Tree and Support Vector Machine. Both algorithms are capable of classifying accurately and effectively with misclassification rate of 0.85%, and 0.94%, respectively. This work shows that classifier using CART provides ease of interpreting the result and implementation.

오늘 날 다양한 플랫폼을 기반으로 한 무선 네트워크 위에 실행되고 있는 수 많은 응용 프로그램은 서비스 운영자 입장에서 정확히 분류해내는 것은 중요하다. 이 연구는 WiBro 상용망에서 임의로 생성한 트래픽 데이터에서 다양한 응용프로그램들을 분류하는 것을 목적으로 한다. 분류기를 개발하는데 있어서 기존에 Flow기반으로 분류를 하는 대신 세션이라는 단위로 실험을 진행하였다. 이 단위를 사용하여 두 가지 분류 기법을 사용하였다. Classification and Regression Tree와 Support Vector Machine. 각 판별기는 생성된 변수들을 기반으로 판별을 시도하였을 때 CART의 경우 0.85%, SVM의 경우 0.94%의 오차를 보여 우수한 성능을 보였지만, 판별기의 구현과 결과 해석이 용이한 CART를 이용하여 판별시스템을 구축하는 것이 유리함을 보였다.

Keywords

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