Hybrid feature selection for phishing email detection
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part II
Using psycholinguistic features for profiling first language of authors
Journal of the American Society for Information Science and Technology
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Security and Communication Networks
A multi-tier phishing detection and filtering approach
Journal of Network and Computer Applications
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Phishing emails have been used widely in fraud of financial organizations and customers. Phishing email detection has drawn a lot attention for many researchers and malicious detection devices are installed in email servers. However, phishing has become more and more complicated and sophisticated and attack can bypass the filter set by anti-phishing techniques. In this paper, we present a method to build a robust classifier to detect phishing emails using hybrid features and to select features using information gain. We experiment on 10 cross-validations to build an initial classifier which performs well. The experiment also analyses the quality of each feature using information gain and best feature set is selected after a recursive learning process. Experimental result shows the selected features perform as well as the original features. Finally, we test five machine learning algorithms and compare the performance of each. The result shows that decision tree builds the best classifier.