A New Meta-Heuristic Based on Social Bees for Detection and Filtering of Spam

A New Meta-Heuristic Based on Social Bees for Detection and Filtering of Spam

Reda Mohamed Hamou (Department of Mathematics and Computer Science, Dr. Moulay Tahar University of SAÏDA, Saida, Algeria), Abdelmalek Amine (Department of Mathematics and Computer Science, Dr. Moulay Tahar University of SAÏDA, Saida, Algeria) and Amine Boudia (Department of Mathematics and Computer Science, Dr. Moulay Tahar University of SAÏDA, Saida, Algeria)
Copyright: © 2013 |Pages: 19
DOI: 10.4018/ijamc.2013070102
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Abstract

Spam is now seized the Internet in phenomenal proportions since a high percentage of total emails exchanged on the Internet. In the fight against spam, the authors are interested in this article experiencing a meta-heuristic based on social bees. The authors took inspiration from biological model of social bees and especially, their organization in the workplace, and collective intelligence. The authors chose this meta-heuristic because it presents effects allow the authors to detect the characteristics of unwanted data. Messages are indexed and represented by the n-gram words and characters independent of languages ??(because a message can be received in any language). The results are promising and provide an important way for the use of this model for solving other problems in data mining.
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State Of The Art

Among the anti-spam techniques exist in the literature include those based on machine learning and those not based on machine learning.

The Techniques Not Based on Machine Learning

Heuristics, or rules-based, the analysis uses regular expression rules to detect phrases or characteristics that are common in spam, and the amount and severity of identified features will propose the appropriate classification of the message. The history and the popularity of this technology have largely been driven by its simplicity, speed and accuracy. In addition, it is better than many advanced technologies of filtering and detection in the sense that it does not require a learning period. Techniques based on signatures generate a unique hash value (signature) for each message recognized spam. Filters signature compare the hash value of all incoming mail against those stored (the hash values ​​previously identified to classify spam e-mail). This kind of technology makes it statistically unlikely that a legitimate email to have the same hash of a spam message. This allows filters signatures to achieve a very low level of false positives. The blacklist is a technique that is simple common among almost all filtration products. Also known as block lists, blacklists filter e-mails from a specific sender. White lists, or lists of authorization, perform the opposite function, to correctly classify an email automatically from a specific sender. (Gyöngyi, Garcia-Molina, & Pedersen, 2004; Byun, Lee, Webb, Irani & Pu, 2009). Currently, there is a spam filtering technology based on traffic analysis provides a characterization of spam traffic patterns where a number of attributes per email are able to identify the characteristics that separate spam traffic from non-spam traffic(Yerazunis, 2004).

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