E-Mail Classification for Phishing Defense

We 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. We introduce various new features for identifying phishing messages 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.

Experiments with representative data sets illustrate that our approach yields better classification results than existing phishing detection methods. Moreover, the direct ternary classification proposed is compared to a sequence of two binary classification processes. Direct one-step ternary classification is not only more efficient, but is also shown to achieve better accuracy than repeated binary classification.