Anomaly internet network traffic detection by kernel principle component classifier

Hanghang Tong, Chongrong Li, Jingrui He, Jiajian Chen, Quang Anh Tran, Haixin Duan, Xing Li

Research output: Contribution to journalConference articlepeer-review

Abstract

As a crucial issue in computer network security, anomaly detection is receiving more and more attention from both application and theoretical point of view. In this paper, a novel anomaly detection scheme is proposed. It can detect anomaly network traffic which has extreme large value on some original feature by the major component, or does not follow the correlation structure of normal traffic by the minor component. By introducing kernel trick, the non-linearity of network traffic can be well addressed. To save the processing time, a simplified version is also proposed, where only major component is adopted. Experimental results validate the effectiveness of the proposed scheme.

Original languageEnglish (US)
Pages (from-to)476-481
Number of pages6
JournalLecture Notes in Computer Science
Volume3498
Issue numberIII
DOIs
StatePublished - 2005
Externally publishedYes
EventSecond International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China
Duration: May 30 2005Jun 1 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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