Article

Unsupervised anomaly detection based n an evolutionary artificial immune network

Authors:

Liu Fang,

Lin Le-PingAuthors Info & Claims

EC'05: Proceedings of the 3rd European conference on Applications of Evolutionary Computing

Pages 166 - 174

https://doi.org/10.1007/978-3-540-32003-6_17

Published: 30 March 2005 Publication History

Abstract

To solve the problem of unsupervised anomaly detection, an unsupervised anomaly-detecting algorithm based on an evolutionary artificial immune network is proposed in this paper. An evolutionary artificial immune network is “evolved” by using unlabeled training sample data to represent the distribution of the original input data set. Then a traditional hierarchical agglomerative clustering method is employed to perform clustering analysis within the algorithm. It is shown that the algorithm is feasible and effective with simulations over the 1999 KDD CUP dataset.

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Cited By

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Wu SBanzhaf W(2010)ReviewApplied Soft Computing10.1016/j.asoc.2009.06.01910:1(1-35)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.asoc.2009.06.019
Lau HTimmis JBate I(2009)Anomaly detection inspired by immune network theoryProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1690005(3045-3051)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1690005
Liu FTian Y(2006)Intrusion detection based on clustering organizational co-evolutionary classificationProceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery10.1007/11881599_139(1113-1120)Online publication date: 24-Sep-2006
https://dl.acm.org/doi/10.1007/11881599_139

Unsupervised anomaly detection based n an evolutionary artificial immune network
1. Computing methodologies

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Published In

EC'05: Proceedings of the 3rd European conference on Applications of Evolutionary Computing

March 2005

628 pages

ISBN:3540253963

Editors:
Franz Rothlauf
Johannes Gutenberg University, Mainz, Germany
,
Jürgen Branke
Institute AIFB, University of Karlsruhe, Karlsruhe, Germany
,
Stefano Cagnoni
Dipartimento di Ingegneria dell'Informazione, Università di Parma, Karlsruhe
,
David Wolfe Corne
School of Mathematical and Computer Science, Heriot-Watt University, Edinburgh, UK
,
Rolf Drechsler
Institute of Computer Science, University of Bremen, Bremen, Germany

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 30 March 2005

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Wu SBanzhaf W(2010)ReviewApplied Soft Computing10.1016/j.asoc.2009.06.01910:1(1-35)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.asoc.2009.06.019
Lau HTimmis JBate I(2009)Anomaly detection inspired by immune network theoryProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1690005(3045-3051)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1690005
Liu FTian Y(2006)Intrusion detection based on clustering organizational co-evolutionary classificationProceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery10.1007/11881599_139(1113-1120)Online publication date: 24-Sep-2006
https://dl.acm.org/doi/10.1007/11881599_139

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An Artificial Immune Clustering Approach to Unsupervised Network Intrusion Detection

An effective unsupervised network anomaly detection method

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