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This paper proposes a novel solution to spam detection inspired by a model ofthe adaptive immune system known as the crossregulation model. We report on thetesting of a preliminary algorithm on six e-mail corpora. We also compare ourresults statically and dynamically with those obtained by the Naive Bayesclassifier and another binary classification method we developed previously forbiomedical text-mining applications. We show that the cross-regulation model iscompetitive against those and thus promising as a bio-inspired algorithm forspam detection in particular, and binary classification in general.

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