Is negative selection appropriate for anomaly detection?

Thomas Stibor*, Philipp Mohr, Jonathan Timmis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

141 Citations (SciVal)


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.

Original languageEnglish
Title of host publicationGECCO 2005 - Genetic and Evolutionary Computation Conference
EditorsH.G. Beyer, U.M. O'Reilly, D. Arnold, W. Banzhaf, C. Blum, E.W. Bonabeau, E. Cantu-Paz, D. Dasgupta, K. Deb, al et al
Number of pages8
Publication statusPublished - 2005
EventGECCO 2005 - Genetic and Evolutionary Computation Conference - Washington, D.C., United States of America
Duration: 25 Jun 200529 Jun 2005

Publication series

NameGECCO 2005 - Genetic and Evolutionary Computation Conference


ConferenceGECCO 2005 - Genetic and Evolutionary Computation Conference
Country/TerritoryUnited States of America
CityWashington, D.C.
Period25 Jun 200529 Jun 2005


  • Anomaly Detection
  • Artificial Immune Systems
  • Negative Selection
  • One-Class SVM
  • Positive Selection

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