Hybrid flexible neural-tree-based intrusion detection systems: Research Articles

Authors:
Yuehui Chen;Ajith Abraham;Bo Yang
Affiliations:
School of Information Science and Engineering, Jinan University, Jinan 250022, P.R. China;School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea;School of Information Science and Engineering, Jinan University, Jinan 250022, P.R. China
Venue:
International Journal of Intelligent Systems
Year:
2007

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Modelling intrusion detection as an allocation problem

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A triangle area based nearest neighbors approach to intrusion detection

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Review: Intrusion detection by machine learning: A review

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A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks

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A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering

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A parallel evolving algorithm for flexible neural tree

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A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection

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The use of artificial-intelligence-based ensembles for intrusion detection: a review

Applied Computational Intelligence and Soft Computing

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Abstract

An intrusion is defined as a violation of the security policy of the system, and, hence, intrusion detection mainly refers to the mechanisms that are developed to detect violations of system security policy. Current intrusion detection systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. This article proposes an IDS model based on a general and enhanced flexible neural tree (FNT). Based on the predefined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, overlayer connections, and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm, and the parameters are optimized by a particle swarm optimization algorithm. Empirical results indicate that the proposed method is efficient. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 337–352, 2007.