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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

227 Scopus Citations
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Author(s)

  • Gang Wang
  • Jinxing Hao
  • Jian Ma
  • Lihua Huang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)6225 - 6232
Journal / PublicationExpert Systems with Applications
Volume37
Issue number9
Publication statusPublished - 2010

Abstract

Many researches have argued that Artificial Neural Networks (ANNs) can improve the performance of intrusion detection systems (IDS) when compared with traditional methods. However for ANN-based IDS, detection precision, especially for low-frequent attacks, and detection stability are still needed to be enhanced. In this paper, we propose a new approach, called FC-ANN, based on ANN and fuzzy clustering, to solve the problem and help IDS achieve higher detection rate, less false positive rate and stronger stability. The general procedure of FC-ANN is as follows: firstly fuzzy clustering technique is used to generate different training subsets. Subsequently, based on different training subsets, different ANN models are trained to formulate different base models. Finally, a meta-learner, fuzzy aggregation module, is employed to aggregate these results. Experimental results on the KDD CUP 1999 dataset show that our proposed new approach, FC-ANN, outperforms BPNN and other well-known methods such as decision tree, the naive Bayes in terms of detection precision and detection stability. (C) 2010 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Intrusion detection systems, Artificial Neural Networks, Fuzzy clustering