open access

Cosdes: A Collaborative Spam Detection System With A Novel E-Mail Abstraction Scheme

  • Nirmala Gandhi J Associate Professor, Department of Computer Science of Engineering ,K.S.R. College of Engineering, Tiruchengode
  • Mohanraj K Student, Department of Computer Science of Engineering ,K.S.R. College of Engineering ,Tiruchengode.
  • Nandhini K Student, Department of Computer Science of Engineering ,K.S.R. College of Engineering, Tiruchengode.
  • Poovarasan K Student, Department of Computer Science of Engineering ,K.S.R. College of Engineering ,Tiruchengode.
  • Prakashraj K Student, Department of Computer Science of Engineering ,K.S.R. College of Engineering ,Tiruchengode.

Abstract

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. This paper proposes a novel e-mail abstraction scheme, which considers e-mail layout structure to represent e-mails. A procedure is presented 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, a complete spam detection system Cosdes (standing for Collaborative Spam Detection System) is designed, 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.

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