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Symbiotic Data Mining for personalized spam filtering

Cortez, P; Lopes, C; Sousa, P; Rocha, M; Rio, M; (2009) Symbiotic Data Mining for personalized spam filtering. In: (pp. pp. 149-156).

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Unsolicited e-mail (spam) is a severe problem due to intrusion of privacy, online fraud, viruses and time spent reading unwanted messages. To solve this issue, Collaborative Filtering (CF) and Content-Based Filtering (CBF) solutions have been adopted. We propose a new CBF-CF hybrid approach called Symbiotic Data Mining (SDM), which aims at aggregating distinct local filters in order to improve filtering at a personalized level using collaboration while preserving privacy. We apply SDM to spam e-mail detection and compare it with a local CBF filter (i.e. Naive Bayes). Several experiments were conducted by using a novel corpus based on the well known Enron datasets mixed with recent spam. The results show that the symbiotic strategy is competitive in performance when compared to CBF and also more robust to contamination attacks. © 2009 IEEE.

Type: Proceedings paper
Title: Symbiotic Data Mining for personalized spam filtering
ISBN-13: 9780769538013
DOI: 10.1109/WI-IAT.2009.30
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/1360113
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