Investigation of fuzzy adaptive resonance theory in network anomaly intrusion detection

Nawa Ngamwitthayanon; Naruemon Wattanapongsakorn; David W. Coit

doi:10.1007/978-3-642-01510-6_24

Investigation of fuzzy adaptive resonance theory in network anomaly intrusion detection

Nawa Ngamwitthayanon, Naruemon Wattanapongsakorn, David W. Coit

School of Engineering, Industrial Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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.

Original language	English (US)
Title of host publication	Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings
Pages	208-217
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-01510-6_24
State	Published - 2009
Event	6th International Symposium on Neural Networks, ISNN 2009 - Wuhan, China Duration: May 26 2009 → May 29 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	5552 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	6th International Symposium on Neural Networks, ISNN 2009
Country/Territory	China
City	Wuhan
Period	5/26/09 → 5/29/09

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Keywords

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

Access to Document

10.1007/978-3-642-01510-6_24

Cite this

Ngamwitthayanon, N., Wattanapongsakorn, N., & Coit, D. W. (2009). Investigation of fuzzy adaptive resonance theory in network anomaly intrusion detection. In Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings (PART 2 ed., pp. 208-217). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5552 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-01510-6_24

Ngamwitthayanon, Nawa ; Wattanapongsakorn, Naruemon ; Coit, David W. / Investigation of fuzzy adaptive resonance theory in network anomaly intrusion detection. Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings. PART 2. ed. 2009. pp. 208-217 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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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.",

keywords = "Adaptive learning, Fuzzy-adaptive resonance theory, Intrusion detection, Network anomaly detection, One shot fast learning",

author = "Nawa Ngamwitthayanon and Naruemon Wattanapongsakorn and Coit, {David W.}",

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booktitle = "Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings",

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Ngamwitthayanon, N, Wattanapongsakorn, N & Coit, DW 2009, Investigation of fuzzy adaptive resonance theory in network anomaly intrusion detection. in Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5552 LNCS, pp. 208-217, 6th International Symposium on Neural Networks, ISNN 2009, Wuhan, China, 5/26/09. https://doi.org/10.1007/978-3-642-01510-6_24

Investigation of fuzzy adaptive resonance theory in network anomaly intrusion detection. / Ngamwitthayanon, Nawa; Wattanapongsakorn, Naruemon; Coit, David W.
Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings. PART 2. ed. 2009. p. 208-217 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5552 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Investigation of fuzzy adaptive resonance theory in network anomaly intrusion detection

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AU - Wattanapongsakorn, Naruemon

AU - Coit, David W.

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N2 - 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.

AB - 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.

KW - Adaptive learning

KW - Fuzzy-adaptive resonance theory

KW - Intrusion detection

KW - Network anomaly detection

KW - One shot fast learning

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Ngamwitthayanon N, Wattanapongsakorn N, Coit DW. Investigation of fuzzy adaptive resonance theory in network anomaly intrusion detection. In Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings. PART 2 ed. 2009. p. 208-217. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-01510-6_24

Investigation of fuzzy adaptive resonance theory in network anomaly intrusion detection

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All Science Journal Classification (ASJC) codes

Keywords

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