Identification of SPAM messages using an approach inspired on the immune system

• Autor(a) principal: Guzella, Thiago dos Santos
• Data de Publicação: 2015
• Outros Autores: Santos, Tomaz Aroldo Mota, Caminhas, Walmir Matos, Uchôa, Joaquim Quinteiro
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/9645
• topic: Artificial immune system; SPAM identification; Continuous learning; Innate and adaptive immunity; Regulatory t cells

In this paper, an immune-inspired model, named innate and adaptive artificial immune system (IA-AIS) is proposed and applied to the problem of identification of unsolicited bulk e-mail messages (SPAM). It integrates entities analogous to macrophages, B and T lymphocytes, modeling both the innate and the adaptive immune systems. An implementation of the algorithm was capable of identifying more than 99% of legitimate or SPAM messages in particular parameter configurations. It was compared to an optimized version of the na¨ıve Bayes classifier, which has been attained extremely high correct classification rates. It has been concluded that IA-AIS has a greater ability to identify SPAM messages, although the identification of legitimate messages is not as high as that of the implemented na¨ıve Bayes classifier. © 2008 Elsevier Ireland Ltd. All rights reserved.