The Impact of the Mode of Data Representation for the Result Quality of the Detection and Filtering of Spam

The Impact of the Mode of Data Representation for the Result Quality of the Detection and Filtering of Spam

Reda Mohamed Hamou (Computer Science Department, Dr Moulay Tahar University of Saïda, Saïda, Algeria) and Abdelmalek Amine (Computer Science Department, Dr Moulay Tahar University of Saïda, Saïda, Algeria)
Copyright: © 2013 |Pages: 17
DOI: 10.4018/ijirr.2013010103
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Abstract

Spam is now seized of the Internet in phenomenal proportions since it high represents a percentage of total emails exchanged on the Internet. In the fight against spam, the authors are interested in this article to develop a hybrid algorithm based primarily on the probabilistic model in this case Naïve Bayes for weighting the terms of the matrix term -category and second place used an algorithm of unsupervised learning (K-means) to filter two classes namely spam and ham. To determine the sensitive parameters that improve the classifications the authors are interested in studying the content of the messages by using a representation of messages by the n-gram words and characters independent of languages (because a message may be received in any language) to later decide what representation opt to get a good classification. The authors have chosen several metrics evaluation to validate their results.
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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 has 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. 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.

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