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Authors discuss a classification-based approach for filtering phishing messages in an e-mail stream. Upon arrival, various features of every e-mail are extracted. This forms the basis of a classification process which detects potentially harmful phishing messages. Authors introduce various new features for identifying phishing e-mail and rank established as well as newly introduced features according to their significance for this classification problem. Moreover, in contrast to classical binary classification approaches (spam vs. not spam), a more refined ternary classification approach for filtering e-mail data is investigated which automatically distinguishes three message types: ham (solicited e-mail), spam, and phishing.
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