Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework

Chi-Ho TSANG, Sam KWONG, Hanli WANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

21 Citations (Scopus)

Abstract

In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpretable fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic. © 2005 IEEE.
Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages789-792
DOIs
Publication statusPublished - 2005
Externally publishedYes

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