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Malicious Web sites largely promote the growth of Internet criminal activities and constrain the development of Web services. As a result, there has been strong motivation to develop systemic solution to stopping the user from visiting such Web sites. In this paper, we propose a learning based approach to classifying Web sites into 3 classes: benign, phishing, and malware. Our mechanism only analyzes the Uniform Resource Locator (URL) itself without accessing the content of Web sites. Thus, it eliminates the run-time latency and the possibility of exposing users to the browserbased vulnerabilities. By employing learning algorithms, our scheme achieves better performance on generality and coverage compared with blacklisting service. Through extensive evaluation, the resulting classifiers obtain 97.53% accuracy on detecting malicious Web sites.