Adaptive Spam Detection Inspired by a Cross-Regulation Model of Immune Dynamics: A Study of Concept Drift

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dc.creatorAbi-Haidar, Alaa
dc.creatorRocha, Luis M.
dc.date2008-12-04
dc.date.accessioned2026-07-07T12:09:26Z
dc.date.available2026-07-07T12:09:26Z
dc.descriptionThis paper proposes a novel solution to spam detection inspired by a model of the adaptive immune system known as the crossregulation model. We report on the testing of a preliminary algorithm on six e-mail corpora. We also compare our results statically and dynamically with those obtained by the Naive Bayes classifier and another binary classification method we developed previously for biomedical text-mining applications. We show that the cross-regulation model is competitive against those and thus promising as a bio-inspired algorithm for spam detection in particular, and binary classification in general.
dc.identifierhttps://arxiv.org/abs/0812.1014
dc.identifierhttp://arxiv.org/abs/0812.1014
dc.identifierArtificial Immune Systems: 7th International Conference, (ICARIS 2008). Bentley, Peter; Lee, Doheon; Jung, Sungwon (Eds.) Lecture Notes in Computer Science. Springer-Verlag, 5132: 36-47
dc.identifierdoi:10.1007/978-3-540-85072-4_4
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/209625
dc.subjectArtificial Intelligence
dc.subjectInformation Retrieval
dc.subjectAdaptation and Self-Organizing Systems
dc.titleAdaptive Spam Detection Inspired by a Cross-Regulation Model of Immune Dynamics: A Study of Concept Drift
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