Abstract
Nowadays e-mail spam is not a novelty, but it is still an important rising problem with a big economic impact in society. Fortunately, there are different approaches able to automatically detect and remove most of those messages, and the best-known ones are based on machine learning techniques, such as Naïve Bayes classifiers and Support Vector Machines. However, there are several different models of Naïve Bayes filters, something the spam literature does not always acknowledge. In this chapter, we present and compare seven different versions of Naïve Bayes classifiers, the well-known linear Support Vector Machine and a new method based on the Minimum Description Length principle. Furthermore, we have conducted an empirical experiment on six public and real non-encoded datasets. The results indicate that the proposed filter is easy to implement, incrementally updateable and clearly outperforms the state-of-the-art spam filters.
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Almeida, T.A., Yamakami, A. (2012). Advances in Spam Filtering Techniques. In: Elizondo, D., Solanas, A., Martinez-Balleste, A. (eds) Computational Intelligence for Privacy and Security. Studies in Computational Intelligence, vol 394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25237-2_12
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DOI: https://doi.org/10.1007/978-3-642-25237-2_12
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