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Visualisation Model Based on Phishing Features

Author

Listed:
  • Majed Rajab

    (Computer Science Department, Eastern Michigan University, MI, USA)

Abstract

The numbers of online purchases and electronic banking transactions have increased substantially in the era of electronic business and mobile commerce. These online financial activities have attracted a special web threat called “phishing” that targets Internet users by seeking their credentials in order to access their financial information. Phishing involves impersonating a legitimate website by creating a visually similar fake website to deceive users. In the last decade different solutions to fight phishing that are primarily based on educating users, user’s experience, search methods, machine learning and features similarity have been developed. This paper combines computational intelligence along with user’s experience approaches to develop an anti-phishing visualisation method. Our method employs effective features chosen following thorough analysis on features scores generated by Correlation Feature Set and Information Gain processing techniques. We validate our anti-phishing features using classification systems produced by rule induction data mining approach. False positives, false negatives and phishing detection rate are the basis of evaluating the classification systems to measure our anti-phishing methods features’ integrity.

Suggested Citation

  • 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.
  • Handle: RePEc:wsi:jikmxx:v:18:y:2019:i:01:n:s0219649219500102
    DOI: 10.1142/S0219649219500102
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Fadi Thabtah, 2006. "Rule Preference Effect in Associative Classification Mining," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 13-20.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

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