This paper proposes a machine-learning framework for supporting intelligent web phishing detection and analysis, and provides its experimental evaluation. In particular we make use of state-of-the-art decision tree algorithms for detecting whether a Web site is able to perform phishing activities. If this is the case, the Web site is classified as a Web-phishing site. Our experimental evaluation confirms the benefits of applying machine learning methods to the well-known web-phishing detection problem.
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