Is negative selection appropriate for anomaly detection?

  • Authors:

  • Thomas Stibor;Philipp Mohr;Jonathan Timmis;Claudia Eckert

  • Affiliations:

  • Darmstadt University of Technology, Darmstadt, Germany;University of Kent;University of Kent;Darmstadt University of Technology, Darmstadt, Germany

  • Venue:
  • GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algorithm in general, when applied to anomaly detection. A straightforward self detector classification principle is proposed and its classification performance is compared to a real-valued negative selection algorithm and to a one-class support vector machine. Earlier work suggests that real-value negative selection requires a single class to learn from. The investigations presented in this paper reveal, however, that when applied to anomaly detection, the real-valued negative selection and self detector classification techniques require positive and negative examples to achieve a high classification accuracy. Whereas, one-class SVMs only require examples from a single class.