Unsolicited commercial email (UCE), also known as spam, has been a major problem on the Internet. In the past, researchers have addressed this problem as a text classification or categorization problem. However, as spammers' techniques continue to evolve and the genre of email content becomes more and more diverse, text-based anti-spam approaches alone are no longer sufficient. In this paper, we propose a novel anti-spam system which utilizes visual clues, in addition to text information in the email body, to determine whether a message is spam. We analyze a large collection of spam emails containing images and identify a number of useful visual features for this application. We then propose using one-class support vector machines (SVM) as the underlying base classifier for anti-spam filtering. The experimental results demonstrate that the proposed system can add significant filtering power to the existing text-based anti-spam filters.


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    Titel :

    Using visual features for anti-spam filtering


    Beteiligte:
    Ching-Tung Wu, (Autor:in) / Kwang-Ting Cheng, (Autor:in) / Qiang Zhu, (Autor:in) / Yi-Leh Wu, (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    132297 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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