Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Introduction to Machine Learning by Ethem Alpaydin, MIT Press, 0-262-01211-1, 400 pp., $50.00/£32.95
The Knowledge Engineering Review
Protecting people from phishing: the design and evaluation of an embedded training email system
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning to detect phishing emails
Proceedings of the 16th international conference on World Wide Web
Anti-Phishing Phil: the design and evaluation of a game that teaches people not to fall for phish
Proceedings of the 3rd symposium on Usable privacy and security
A comparison of machine learning techniques for phishing detection
Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit
E-Mail Classification for Phishing Defense
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
New filtering approaches for phishing email
Journal of Computer Security - EU-Funded ICT Research on Trust and Security
Phishnet: predictive blacklisting to detect phishing attacks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
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Phishing is a semantic attack that aims to take advantage of the naivety of users of electronic services (e.g. e-banking). A number of solutions have been proposed to minimize the impact of phishing attacks. The most accurate email phishing classifiers, that are publicly known, use machine learning techniques. Previous work in phishing email classification via machine learning have primarily focused on enhancing the classification accuracy by studying the addition of novel features, ensembles, or classification algorithms. This study follows a different path by taking advantage of previously proposed features. The primary focus of this paper is to enhance the classification accuracy of phishing email classifiers by finding an effective feature subset out of a number of previously proposed features, by evaluating various feature selection methods. The selected feature subset in this study resulted in a classification model with an f1 score of 99.396% for 21 heuristic features and a single classifier.