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A Comparative Study of Real-Valued Negative Selection to Statistical Anomaly Detection Techniques

  • Thomas Stibor
  • Jonathan Timmis
  • Claudia Eckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3627)

Abstract

The (randomized) real-valued negative selection algorithm is an anomaly detection approach, inspired by the negative selection immune system principle. The algorithm was proposed to overcome scaling problems inherent in the hamming shape-space negative selection algorithm. In this paper, we investigate termination behavior of the real-valued negative selection algorithm with variable-sized detectors on an artificial data set. We then undertake an analysis and comparison of the classification performance on the high-dimensional KDD data set of the real-valued negative selection, a real-valued positive selection and statistical anomaly detection techniques. Results reveal that in terms of detection rate, real-valued negative selection with variable-sized detectors is not competitive to statistical anomaly detection techniques on the KDD data set. In addition, we suggest that the termination guarantee of the real-valued negative selection with variable-sized detectors is very sensitive to several parameters.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Thomas Stibor
    • 1
  • Jonathan Timmis
    • 2
  • Claudia Eckert
    • 1
  1. 1.Department of Computer ScienceDarmstadt University of Technology 
  2. 2.Departments of Electronics and Computer ScienceUniversity of YorkHeslington, York

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