Deriving Correlated Sets of Website Features for Phishing Detection: A Computational Intelligence Approach

Fadi Thabtah () and Neda Abdelhamid ()
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Fadi Thabtah: Applied Business and Computing, Nelson Marlborough Institute of Technology, Auckland, New Zealand
Neda Abdelhamid: Information Technology, Auckland Institute of Studies, Auckland, New Zealand

Journal of Information & Knowledge Management (JIKM), 2016, vol. 15, issue 04, 1-17

Abstract: Classification is one of the major tasks in data mining which aims to build classifiers for decision making. One of the most recent online threats is phishing, which has caused significant losses to online shoppers, electronic businesses and financial institutions. A common way of phishing is impersonating online websites to deceive online users and steal their financial information. One way to guide the anti-phishing classification method is to preliminarily identify a minimal set of related features so the search space can be reduced. The aim of this paper is to compare different features assessment techniques in the website phishing context in order to determine the minimal set of features for detecting phishing activities. Experimental results on real phishing datasets consisting of 30 features has been conducted using three known features selection methods. New features cutoffs have been identified after statistical analysis utilising three data mining classification methods. We have been able to identify new clusters of features that when used together are able to detect phishing activities. Further, important correlations among common features have been derived.

Keywords: Classification; data mining; feature selection; web security; information gain; phishing (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1142/S0219649216500428

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