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Anomaly detection inspired by immune network theory: A proposal
University of York.
University of York.
University of York.ORCID iD: 0000-0003-2415-8219
2009 (English)In: 2009 IEEE Congress on Evolutionary Computation, CEC 2009, 2009, 3045-3051 p.Conference paper (Refereed)
Abstract [en]

Previous research in supervised and unsupervised anomaly detection normally employ a static model of normal behaviour (normal-model) throughout the lifetime of the system. However, there are real world applications such as swarm robotics and wireless sensor networks where what is perceived as normal behaviour changes accordingly to the changes in the environment. To cater for such systems, dynamically updating the normal-model is required. In this paper, we examine the requirements from a range of distributed autonomous systems and then propose a novel unsupervised anomaly detection architecture capable of online adaptation inspired by the vertebrate immune system.

Place, publisher, year, edition, pages
2009. 3045-3051 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-23799DOI: 10.1109/CEC.2009.4983328ISI: 000274803101141Scopus ID: 2-s2.0-70450059393ISBN: 9781424429592 (print)OAI: oai:DiVA.org:mdh-23799DiVA: diva2:682468
Conference
2009 IEEE Congress on Evolutionary Computation, CEC 2009; Trondheim; Norway; 18 May 2009 through 21 May 2009
Available from: 2013-12-27 Created: 2013-12-19 Last updated: 2014-05-16Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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