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Temporal Anomaly Detection: An Artificial Immune Approach Based on T-Cell Activation, Clonal Size Regulation and Homeostasis

2010Conference paper Open access
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datacite.subject.fosCiências Médicas::Medicina Básica
datacite.subject.fosCiências Naturais::Ciências Biológicas
dc.contributor.authorAntunes, Mário J.
dc.contributor.authorCorreia, Manuel E.
dc.date.accessioned2025-10-31T12:36:50Z
dc.date.available2025-10-31T12:36:50Z
dc.date.issued2010
dc.descriptionEISBN - 9781441959133
dc.descriptionFonte: https://repositorio.inesctec.pt/items/c35955e7-8639-41f5-9b9b-179c1a80beed
dc.description.abstractThis paper presents an artificial immune system (AIS) based on Grossman's tunable activation threshold (TAT) for temporal anomaly detection. We describe the generic AIS framework and the TAT model adopted for simulating T Cells behaviour, emphasizing two novel important features: the temporal dynamic adjustment of T Cells clonal size and its associated homeostasis mechanism. We also present some promising results obtained with artificially generated data sets, aiming to test the appropriateness of using TAT in dynamic changing environments, to distinguish new unseen patterns as part of what should be detected as normal or as anomalous. We conclude by discussing results obtained thus far with artificially generated data sets.eng
dc.identifier.citationAntunes, M.J., Correia, M.E. (2010). Temporal Anomaly Detection: An Artificial Immune Approach Based on T Cell Activation, Clonal Size Regulation and Homeostasis. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_33.
dc.identifier.doi10.1007/978-1-4419-5913-3_33
dc.identifier.eissn2214-8019
dc.identifier.isbn9781441959126
dc.identifier.isbn9781441959133
dc.identifier.issn0065-2598
dc.identifier.urihttp://hdl.handle.net/10400.8/14443
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-1-4419-5913-3_33
dc.relation.ispartofAdvances in Experimental Medicine and Biology
dc.relation.ispartofAdvances in Computational Biology
dc.rights.uriN/A
dc.subjectArtificial immune systems
dc.subjectPattern recognition
dc.subjectAnomaly detection
dc.subjectHomeostasis
dc.titleTemporal Anomaly Detection: An Artificial Immune Approach Based on T-Cell Activation, Clonal Size Regulation and Homeostasiseng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2010
oaire.citation.titleAdvances in Experimental Medicine and Biology
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
person.familyNameAntunes
person.givenNameMário
person.identifierR-000-NX4
person.identifier.ciencia-idAF10-7EDD-5153
person.identifier.orcid0000-0003-3448-6726
person.identifier.scopus-author-id25930820200
relation.isAuthorOfPublicatione3e87fb0-d1d6-44c3-985d-920a5560f8c1
relation.isAuthorOfPublication.latestForDiscoverye3e87fb0-d1d6-44c3-985d-920a5560f8c1

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This paper presents an artificial immune system (AIS) based on Grossman's tunable activation threshold (TAT) for temporal anomaly detection. We describe the generic AIS framework and the TAT model adopted for simulating T Cells behaviour, emphasizing two novel important features: the temporal dynamic adjustment of T Cells clonal size and its associated homeostasis mechanism. We also present some promising results obtained with artificially generated data sets, aiming to test the appropriateness of using TAT in dynamic changing environments, to distinguish new unseen patterns as part of what should be detected as normal or as anomalous. We conclude by discussing results obtained thus far with artificially generated data sets.
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