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Investigation of Fuzzy Adaptive Resonance Theory in Network Anomaly Intrusion Detection

  • Nawa Ngamwitthayanon
  • Naruemon Wattanapongsakorn
  • David W. Coit
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)

Abstract

The effectiveness of Fuzzy-Adaptive Resonance Theory (Fuzzy-ART or F-ART) is investigated for a Network Anomaly Intrusion Detection (NAID) application. F-ART is able to group similar data instances into clusters. Furthermore, F-ART is an online clustering algorithm that can learn and update its knowledge based on the presence of new instances to the existing clusters. We investigate a one shot fast learning option of F-ART on the network anomaly detection based on KDD CUP ’99 evaluation data set and found its effectiveness and robustness to such problems along with the fast response capability that can be applied to provide a real-time detection system.

Keywords

Network Anomaly Detection Intrusion Detection Fuzzy-Adaptive Resonance Theory Adaptive Learning One Shot Fast Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nawa Ngamwitthayanon
    • 1
  • Naruemon Wattanapongsakorn
    • 1
  • David W. Coit
    • 2
  1. 1.Department of Computer EngineeringKing Mongkut’s University of Technology ThonburiBangkokThailand
  2. 2.Department of Industrial and Systems EngineeringRutgers UniversityPiscatawayUSA

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