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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Fadi Thabtah & Neda Abdelhamid, 2016. "Deriving Correlated Sets of Website Features for Phishing Detection: A Computational Intelligence Approach," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-17, December.
    • Handle: RePEc:wsi:jikmxx:v:15:y:2016:i:04:n:s0219649216500428
    DOI: 10.1142/S0219649216500428
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    Citations

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    Cited by:

    1. Anthony Gramaje & Fadi Thabtah & Neda Abdelhamid & Sayan Kumar Ray, 2021. "Patient Discharge Classification Using Machine Learning Techniques," Annals of Data Science, Springer, vol. 8(4), pages 755-767, December.
    2. Firuz Kamalov & Fadi Thabtah, 2017. "A Feature Selection Method Based on Ranked Vector Scores of Features for Classification," Annals of Data Science, Springer, vol. 4(4), pages 483-502, December.
    3. Fadi Thabtah & Li Zhang & Neda Abdelhamid, 2019. "NBA Game Result Prediction Using Feature Analysis and Machine Learning," Annals of Data Science, Springer, vol. 6(1), pages 103-116, March.
    4. Majed Rajab, 2019. "Visualisation Model Based on Phishing Features," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-17, March.
    5. Fadi Thabtah & Firuz Kamalov, 2017. "Phishing Detection: A Case Analysis on Classifiers with Rules Using Machine Learning," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1-16, December.

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