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Ensemble of one-class classifiers for network intrusion detection system

Zainal, Anazida and Maarof, Mohd Aizaini and Shamsuddin, Siti Mariyam and Abraham, Ajith P. (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

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Official URL: http://dx.doi.org/10.1109/IAS.2008.35

Abstract

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.

Item Type:Book Section
Additional Information:ISBN: 978-076953324-7; 4th International Symposium on Information Assurance and Security, IAS 2008; Napoli; 8 September 2008 through 10 September 2008
Uncontrolled Keywords:agglomeration, alarm systems, classifiers, computer crime, computer networks, computer programming, data structures, fuzzy inference, fuzzy systems, genetic algorithms, genetic programming, internet, learning algorithms, learning systems, linear programming, security of data, signal detection
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:12556
Deposited By: Liza Porijo
Deposited On:08 Jun 2011 08:10
Last Modified:08 Jun 2011 08:10

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