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

Chi-Ho TSANG; Sam KWONG; Hanli WANG

doi:10.1109/ICDM.2005.26

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 Proceedings › Conference paper (refereed) › Research › peer-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 language	English
Title of host publication	Proceedings of the Fifth IEEE International Conference on Data Mining
Editors	Jiawei HAN, Benjamin W. WAH, Vijay RAGHAVAN, Xindong WU, Rajeev RASTOGI
Publisher	IEEE
Pages	789-792
Number of pages	4
ISBN (Print)	0769522785
DOIs	https://doi.org/10.1109/ICDM.2005.26
Publication status	Published - 2005
Externally published	Yes
Event	Fifth IEEE International Conference on Data Mining - Houston, United States Duration: 27 Nov 2005 → 30 Nov 2005

Conference

Conference	Fifth IEEE International Conference on Data Mining
Abbreviated title	ICDM'05
Country/Territory	United States
City	Houston
Period	27/11/05 → 30/11/05

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10.1109/ICDM.2005.26

Cite this

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

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. {\textcopyright} 2005 IEEE.",

author = "Chi-Ho TSANG and Sam KWONG and Hanli WANG",

year = "2005",

doi = "10.1109/ICDM.2005.26",

language = "English",

isbn = "0769522785",

pages = "789--792",

editor = "Jiawei HAN and WAH, {Benjamin W.} and Vijay RAGHAVAN and Xindong WU and Rajeev RASTOGI",

booktitle = "Proceedings of the Fifth IEEE International Conference on Data Mining",

publisher = "IEEE",

note = "Fifth IEEE International Conference on Data Mining, ICDM'05 ; Conference date: 27-11-2005 Through 30-11-2005",

}

TSANG, C-H, KWONG, S & WANG, H 2005, Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework. in J HAN, BW WAH, V RAGHAVAN, X WU & R RASTOGI (eds), Proceedings of the Fifth IEEE International Conference on Data Mining. IEEE, pp. 789-792, Fifth IEEE International Conference on Data Mining, Houston, Texas, United States, 27/11/05. https://doi.org/10.1109/ICDM.2005.26

Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework. / TSANG, Chi-Ho; KWONG, Sam; WANG, Hanli.
Proceedings of the Fifth IEEE International Conference on Data Mining. ed. / Jiawei HAN; Benjamin W. WAH; Vijay RAGHAVAN; Xindong WU; Rajeev RASTOGI. IEEE, 2005. p. 789-792.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - WANG, Hanli

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N2 - 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.

AB - 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.

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DO - 10.1109/ICDM.2005.26

M3 - Conference paper (refereed)

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EP - 792

BT - Proceedings of the Fifth IEEE International Conference on Data Mining

A2 - HAN, Jiawei

A2 - WAH, Benjamin W.

A2 - RAGHAVAN, Vijay

A2 - WU, Xindong

A2 - RASTOGI, Rajeev

PB - IEEE

T2 - Fifth IEEE International Conference on Data Mining

Y2 - 27 November 2005 through 30 November 2005

ER -

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

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