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Issue No. 05 - May (2011 vol. 23)
ISSN: 1041-4347
pp: 669-682
Chi-Yao Tseng , National Taiwan University and Academia Sinica, Taipei
Pin-Chieh Sung , National Taiwan University, Taipei
Ming-Syan Chen , National Taiwan University and Academia Sinica, Taipei
E-mail communication is indispensable nowadays, but the e-mail spam problem continues growing drastically. In recent years, the notion of collaborative spam filtering with near-duplicate similarity matching scheme has been widely discussed. The primary idea of the similarity matching scheme for spam detection is to maintain a known spam database, formed by user feedback, to block subsequent near-duplicate spams. On purpose of achieving efficient similarity matching and reducing storage utilization, prior works mainly represent each e-mail by a succinct abstraction derived from e-mail content text. However, these abstractions of e-mails cannot fully catch the evolving nature of spams, and are thus not effective enough in near-duplicate detection. In this paper, we propose a novel e-mail abstraction scheme, which considers e-mail layout structure to represent e-mails. We present a procedure to generate the e-mail abstraction using HTML content in e-mail, and this newly devised abstraction can more effectively capture the near-duplicate phenomenon of spams. Moreover, we design a complete spam detection system Cosdes (standing for COllaborative Spam DEtection System), which possesses an efficient near-duplicate matching scheme and a progressive update scheme. The progressive update scheme enables system Cosdes to keep the most up-to-date information for near-duplicate detection. We evaluate Cosdes on a live data set collected from a real e-mail server and show that our system outperforms the prior approaches in detection results and is applicable to the real world.
Spam detection, e-mail abstraction, near-duplicate matching.

M. Chen, P. Sung and C. Tseng, "Cosdes: A Collaborative Spam Detection System with a Novel E-Mail Abstraction Scheme," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 669-682, 2010.
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