Generalization regions in hamming negative selection

Thomas Stibor*, Jonathan Timmis, Claudia Eckert

*Corresponding author for this work

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

7 Citations (SciVal)

Abstract

Negative selection is an immune-inspired algorithm which is typically applied to anomaly detection problems. We present an empirical investigation of the generalization capability of the Hamming negative selection, when combined with the r-chunk affinity metric. Our investigations reveal that when using the r-chunk metric, the length r is a crucial parameter and is inextricably linked to the input data being analyzed. Moreover, we propose that input data with different characteristics, i.e. different positional biases, can result in an incorrect generalization effect.

Original languageEnglish
Title of host publicationIntelligent Information Processing and Web Mining
EditorsMieczyslaw Klopotek, Krzysztof Trojanowski, Slawomir Wierzchon
PublisherSpringer Nature
Pages447-456
Number of pages10
ISBN (Print)9783540335207
DOIs
Publication statusPublished - 2006

Publication series

NameAdvances in Soft Computing
Volume35
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

Keywords

  • negative selection
  • intrusion detection
  • positional bias
  • semantic representation
  • generalization region

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