Ensemble of one-class classifiers for network intrusion detection system
To achieve high accuracy while lowering false alarm rates are major challenges in designing an intrusion detection system. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each uses different learning paradigms. The techniques deployed in this ensemble model a...
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2008
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my.utm.125562017-10-02T08:45:28Z http://eprints.utm.my/id/eprint/12556/ Ensemble of one-class classifiers for network intrusion detection system Zainal, Anazida Maarof, Mohd. Aizaini Shamsuddin, Siti Mariyam Abraham, Ajith QA75 Electronic computers. Computer science To achieve high accuracy while lowering false alarm rates are major challenges in designing an intrusion detection system. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each uses different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (ANFIS) and Random Forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Empirical results show an improvement in detection accuracy for all classes of network traffic; Normal, Probe, DoS, U2R and R2L. RF, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it. Institute of Electrical and Electronics Engineers 2008 Book Section PeerReviewed Zainal, Anazida and Maarof, Mohd. Aizaini and Shamsuddin, Siti Mariyam and Abraham, Ajith (2008) Ensemble of one-class classifiers for network intrusion detection system. In: Proceedings - The 4th International Symposium on Information Assurance and Security, IAS 2008. Institute of Electrical and Electronics Engineers, New York, 180 -185 . ISBN 978-076953324-7 http://dx.doi.org/10.1109/IAS.2008.35 DOI:10.1109/IAS.2008.35 |
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QA75 Electronic computers. Computer science Zainal, Anazida Maarof, Mohd. Aizaini Shamsuddin, Siti Mariyam Abraham, Ajith Ensemble of one-class classifiers for network intrusion detection system |
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To achieve high accuracy while lowering false alarm rates are major challenges in designing an intrusion detection system. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each uses different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (ANFIS) and Random Forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Empirical results show an improvement in detection accuracy for all classes of network traffic; Normal, Probe, DoS, U2R and R2L. RF, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it. |
format |
Book Section |
author |
Zainal, Anazida Maarof, Mohd. Aizaini Shamsuddin, Siti Mariyam Abraham, Ajith |
author_facet |
Zainal, Anazida Maarof, Mohd. Aizaini Shamsuddin, Siti Mariyam Abraham, Ajith |
author_sort |
Zainal, Anazida |
title |
Ensemble of one-class classifiers for network intrusion detection system |
title_short |
Ensemble of one-class classifiers for network intrusion detection system |
title_full |
Ensemble of one-class classifiers for network intrusion detection system |
title_fullStr |
Ensemble of one-class classifiers for network intrusion detection system |
title_full_unstemmed |
Ensemble of one-class classifiers for network intrusion detection system |
title_sort |
ensemble of one-class classifiers for network intrusion detection system |
publisher |
Institute of Electrical and Electronics Engineers |
publishDate |
2008 |
url |
http://eprints.utm.my/id/eprint/12556/ http://dx.doi.org/10.1109/IAS.2008.35 |
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1643645985006551040 |
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13.18916 |