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A preliminary study on why using the nonself detector set for anomaly detection in artificial immune Systems

  • Baoliang Xu*
  • , Wenjian Luo
  • , Xufa Wang
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In artificial immune systems, the detectors in the nonself space are often adopted to detect anomaly changes, such as the negative selection algorithm and its improvements. Since the detectors in the self space can also be used to detect anomaly changes, a frequently asked question is which kind of detector sets is more efficient for a specific problem. In this paper, firstly, the advantages and disadvantages of the self detector set and the nonself detector set are briefly reviewed. Secondly, when the complete matching rule is adopted, the average time costs of employing the nonself detector set and the self detector set for anomaly detection are compared theoretically. Thirdly, simulated experiments are done, and experimental results demonstrate that the theoretical conclusion is essentially correct.

Original languageEnglish
Title of host publicationCIS 2009 - 2009 International Conference on Computational Intelligence and Security
Pages559-564
Number of pages6
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 International Conference on Computational Intelligence and Security, CIS 2009 - Beijing, China
Duration: 11 Dec 200914 Dec 2009

Publication series

NameCIS 2009 - 2009 International Conference on Computational Intelligence and Security
Volume1

Conference

Conference2009 International Conference on Computational Intelligence and Security, CIS 2009
Country/TerritoryChina
CityBeijing
Period11/12/0914/12/09

Keywords

  • Evolutionary negative selection algorithm
  • Negative selection algorithm
  • Nonself
  • Self

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