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Data Analytics: Intelligent Anti-Phishing Techniques Based on Machine Learning

Author

Listed:
  • Said Baadel

    (Faculty of Communication, Arts, and Sciences, Canadian University Dubai, Dubai, UAE2School of Computing and Engineering, University of Huddersfield, Huddersfield, UK)

  • Joan Lu

    (School of Computing and Engineering, University of Huddersfield, Huddersfield, UK)

Abstract

According to the international body Anti-Phishing Work Group (APWG), phishing activities have skyrocketed in the last few years and more online users are becoming susceptible to phishing attacks and scams. While many online users are vulnerable and naive to the phishing attacks, playing catch-up to the phishers’ evolving strategies is not an option. Machine Learning techniques play a significant role in developing effective anti-phishing models. This paper looks at phishing as a classification problem and outlines some of the recent intelligent machine learning techniques (associative classifications, dynamic self-structuring neural network, dynamic rule-induction, etc.) in the literature that is used as anti-phishing models. The purpose of this review is to serve researchers, organisations’ managers, computer security experts, lecturers, and students who are interested in understanding phishing and its corresponding intelligent solutions. This will equip individuals with knowledge and skills that may prevent phishing on a wider context within the community.

Suggested Citation

  • Said Baadel & Joan Lu, 2019. "Data Analytics: Intelligent Anti-Phishing Techniques Based on Machine Learning," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-17, March.
    • Handle: RePEc:wsi:jikmxx:v:18:y:2019:i:01:n:s0219649219500059
    DOI: 10.1142/S0219649219500059
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    References listed on IDEAS

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    1. Neda Abdelhamid & Fadi Thabtah, 2014. "Associative Classification Approaches: Review and Comparison," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-30.
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