NDLI: Ensemble of One-Class Classifiers for Network Intrusion Detection System

Content Provider	IEEE Xplore Digital Library
Author	Zainal, A. Maarof, M.A. Shamsuddin, S.M. Abraham, A.
Copyright Year	2008
Description	Author affiliation: Fac. of Comput. Sci. & Inf. Syst., Univ. Teknol. Malaysia, Skudai (Zainal, A.)
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.
Starting Page	180
Ending Page	185
File Size	230679
Page Count	6
File Format	PDF
ISBN	9780769533247
DOI	10.1109/IAS.2008.35
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-08
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Radio frequency Classification algorithms Classification tree analysis Probes Intrusion detection Genetic programming Random Forest ensemble classifiers Linear Genetic Programming ANFIS
Content Type	Text
Resource Type	Article

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