Genetic Programming and Evolvable Machines

, Volume 4, Issue 4, pp 383–403

Anomaly Detection Using Real-Valued Negative Selection

  • Fabio A. González
  • Dipankar Dasgupta
Article

DOI: 10.1023/A:1026195112518

Cite this article as:
González, F.A. & Dasgupta, D. Genet Program Evolvable Mach (2003) 4: 383. doi:10.1023/A:1026195112518

Abstract

This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organizing maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.

artificial immune systems anomaly detection negative selection matching rule self-organizing maps 

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Fabio A. González
    • 1
    • 2
  • Dipankar Dasgupta
    • 1
  1. 1.Division of Computer ScienceThe University of MemphisMemphis
  2. 2.Departamento de Ingeniería de SistemasUniversidad Nacional de ColombiaColombia

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