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Last Modified: December 31, 2008
Citation: A. Abi-Haidar and L.M. Rocha [2008]. "Adaptive Spam Detection Inspired by the Immune System". In: Artificial Life XI: Eleventh International Conference on the Simulation and Synthesis of Living Systems. S. Bullock, J. Noble, R. A. Watson, and M. A. Bedau (Eds.). MIT Press, pp. 1-8.
The pre-print is available in Adobe Acrobat (.pdf) format only. Due to mathematical notation and graphics, only the abstract is presented here. [BibTex]
Abstract.
This paper proposes a novel solution to spam detection inspired by a model of the adaptive immune system known as the cross-regulation model. We report on the testing of a preliminary algorithm on six e-mail corpora. We also compare our results with those obtained by the Naive Bayes classifier and another binary classification method we developed previously for biomedical text-mining applications. We obtained very encouraging results which can be further improved with development of this bio-inspired model. We show that the cross-regulation model is promising as a bio-inspired algorithm for spam detection in particular, and binary classification in general. Finally, we also present evidence that our bio-inspired model is relevant for understanding immune regulation itself.
Keywords:artificial immune systems, spam detection, cross-regulation, artificial life, biologically-inspired computing, computational intelligence, machine learning.
