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A New Meta-Heuristic based on Human Renal Function for Detection and Filtering of SPAM

A New Meta-Heuristic based on Human Renal Function for Detection and Filtering of SPAM

Mohamed Amine Boudia, Reda Mohamed Hamou, Abdelmalek Amine
Copyright: © 2015 |Volume: 9 |Issue: 4 |Pages: 33
ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781466676381|DOI: 10.4018/IJISP.2015100102
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MLA

Boudia, Mohamed Amine, et al. "A New Meta-Heuristic based on Human Renal Function for Detection and Filtering of SPAM." IJISP vol.9, no.4 2015: pp.26-58. http://doi.org/10.4018/IJISP.2015100102

APA

Boudia, M. A., Hamou, R. M., & Amine, A. (2015). A New Meta-Heuristic based on Human Renal Function for Detection and Filtering of SPAM. International Journal of Information Security and Privacy (IJISP), 9(4), 26-58. http://doi.org/10.4018/IJISP.2015100102

Chicago

Boudia, Mohamed Amine, Reda Mohamed Hamou, and Abdelmalek Amine. "A New Meta-Heuristic based on Human Renal Function for Detection and Filtering of SPAM," International Journal of Information Security and Privacy (IJISP) 9, no.4: 26-58. http://doi.org/10.4018/IJISP.2015100102

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Abstract

The e-mail is therefore one of the most used methods for its efficiency and profitability. In the last few years, the undesirables emails (SPAM) are widely spread as they play an important part in the inbox. Consequently, several recent studies have provided evidence of the importance of detection and filtering of SPAM as a major interest for the Internet community. In the present paper, the authors propose and experiment a new and original meta-heuristic based on the renal system for detection and filtering spam. The natural model of the renal system is taken as an inspiration for its purification of blood, the filtering of toxins as well as the regularization of the blood pressure. The messages are represented by both a bag words and N-Gram method which is independent of languages because an email can be received in any language. After that, the authors propose to use two models to apply a Bayesien classification on textual data: Bernoulli or Multinomial model.

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