  This 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.
