An immunological filter for spam

Authors:
George B. Bezerra;Tiago V. Barra;Hamilton M. Ferreira;Helder Knidel;Leandro Nunes de Castro;Fernando J. Von Zuben
Affiliations:
Laboratory of Bioinformatics and Bio-Inspired Computing (LBIC), Department of Computer Engineering and Industrial Automation, University of Campinas, Unicamp, Campinas, SP, Brazil;Laboratory of Bioinformatics and Bio-Inspired Computing (LBIC), Department of Computer Engineering and Industrial Automation, University of Campinas, Unicamp, Campinas, SP, Brazil;Laboratory of Bioinformatics and Bio-Inspired Computing (LBIC), Department of Computer Engineering and Industrial Automation, University of Campinas, Unicamp, Campinas, SP, Brazil;Laboratory of Bioinformatics and Bio-Inspired Computing (LBIC), Department of Computer Engineering and Industrial Automation, University of Campinas, Unicamp, Campinas, SP, Brazil;Catholic University of Santos, UniSantos, Santos, SP, Brazil;Laboratory of Bioinformatics and Bio-Inspired Computing (LBIC), Department of Computer Engineering and Industrial Automation, University of Campinas, Unicamp, Campinas, SP, Brazil
Venue:
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Year:
2006

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Abstract

Spam messages are continually filling email boxes of practically every Web user. To deal with this growing problem, the development of high-performance filters to block those unsolicited messages is strongly required. An Antibody Network, more precisely SRABNET (Supervised Real-Valued Antibody Network), is proposed as an alternative filter to detect spam. The model of the antibody network is generated automatically from the training dataset and evaluated on unseen messages. We validate this approach using a public corpus, called PU1, which has a large collection of encrypted personal e-mail messages containing legitimate messages and spam. Finally, we compared the performance with the well known naïve Bayes filter using some performances indexes that will be presented.