Self-organizing maps in the design of anti-spam filters a proposal based on thematic categories

Abstract

Spam, or unsolicited messages sent massively, is one of the threats that affects email and other media. Its high volume generates substantial time and economic losses. A solution to this problem is presented: a hybrid anti-spam filter based on unsupervised Artificial Neural Networks (ANNs). It consists of two steps, preprocessing and processing, both based on different computation models: programmed and neural (using Kohonen SOM). This system has been optimized using, as a data corpus, ham from "Enron Email" and spam from two different sources: traditional (user's inbox) and spamtrap-honeypot. It has been proved that thematic categories can be found both in spam and ham words. 1260 system configurations were analyzed, comparing their quality and performance with the most used metrics. All of them achieved AUC > 0.90 and the best 204 AUC > 0.95, despite just using 13 attributes for the input vectors of the SOM, one for each thematic category. Results were similar to other researchers' over the same corpus, though they make use of different Machine Learning (ML) methods and a number of attributes several orders of magnitude greater. It was further tested with datasets not utilized during design, obtaining 0.77 < AUC < 0.96 with normalized data.