Web phishing detection using classifier ensemble

Authors:
Nuttapong Sanglerdsinlapachai;Arnon Rungsawang
Affiliations:
Kasetsart University, Bangkok, Thailand and National Electronics and Computer Technology Center, Pathumthani, Thailand;Kasetsart University, Bangkok, Thailand
Venue:
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Year:
2010

Citing 7
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Abstract

This research adapts and develops various methods in Artificial Intelligent (A.I) field to improve web phishing detection. Based on the features from Carnegie Mellon Anti-phishing and Network Analysis Tool (CANTINA), we add, modify or reduce features in case of using to train a machine learning method. We also add our developed features called homepage similarity features to the machine. Moreover, we applied the classifier ensemble concept to the study. After training with 500 phishing web pages and 500 non-phishing web pages, the experiments on 1,500 pages per each class showed that our proposed methodology could boost accuracy up to approximately 30% from traditional heuristic method's results.