Investigation of fuzzy adaptive resonance theory in network anomaly intrusion detection

Conference proceedings article

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Publication Details

Author list: Ngamwitthayanon N., Wattanapongsakorn N., Coit D.W.

Publisher: Springer

Publication year: 2009

Volume number: 5552 LNCS

Issue number: PART 2

Start page: 208

End page: 217

Number of pages: 10

ISBN: 3642015093; 9783642015090

ISSN: 0302-9743

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-69849108915&doi=10.1007%2f978-3-642-01510-6_24&partnerID=40&md5=e659fed475e1c2ee79be0f0bf882fb8f

Languages: English-Great Britain (EN-GB)

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Abstract

The effectiveness of Fuzzy-Adaptive Resonance Theory (Fuzzy-ART or F-ART) is investigated for a Network Anomaly Intrusion Detection (NAID) application. F-ART is able to group similar data instances into clusters. Furthermore, F-ART is an online clustering algorithm that can learn and update its knowledge based on the presence of new instances to the existing clusters. We investigate a one shot fast learning option of F-ART on the network anomaly detection based on KDD CUP '99 evaluation data set and found its effectiveness and robustness to such problems along with the fast response capability that can be applied to provide a real-time detection system. ฉ 2009 Springer Berlin Heidelberg.

Keywords

Adaptive Learning, Fuzzy-adaptive resonance theory, Network anomaly detection, One shot fast learning