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An Efficient Phishing Website Detection Plugin Service Based on Website Trustworthiness Evaluation

Author

Listed:
  • Phoon Gar Chi

    (Tunku Abdul Rahman University of Management and Technology (TAR UMT), Jalan Genting Kelang, Setapak, 53300 Kuala Lumpur, Federal Territory of Kuala Lumpur.)

  • Low Choon Keat

    (Tunku Abdul Rahman University of Management and Technology (TAR UMT), Jalan Genting Kelang, Setapak, 53300 Kuala Lumpur, Federal Territory of Kuala Lumpur.)

  • Ng Yen Phing

    (Tunku Abdul Rahman University of Management and Technology (TAR UMT), Jalan Genting Kelang, Setapak, 53300 Kuala Lumpur, Federal Territory of Kuala Lumpur.)

  • Tan Bee Sian

    (Tunku Abdul Rahman University of Management and Technology (TAR UMT), Jalan Genting Kelang, Setapak, 53300 Kuala Lumpur, Federal Territory of Kuala Lumpur.)

  • Chong Kim Soon

    (Tunku Abdul Rahman University of Management and Technology (TAR UMT), Jalan Genting Kelang, Setapak, 53300 Kuala Lumpur, Federal Territory of Kuala Lumpur.)

Abstract

Phishing attacks remain a significant threat to online users, and existing anti-phishing mechanisms often fall short in providing real-time, adaptive protection. This study proposes a browser extension that leverages machine learning to assess website trustworthiness and detect phishing threats in real time. A logistic regression classifier was trained using axx dataset comprising both legitimate and phishing URLs sourced from PhishTank and Google’s site rankings. The model achieved an accuracy of 0.96 on training data and 0.91 on test data. Our browser extension provides a robust, real-time defense against phishing, with minimal latency and a user-friendly interface.

Suggested Citation

  • Phoon Gar Chi & Low Choon Keat & Ng Yen Phing & Tan Bee Sian & Chong Kim Soon, 2025. "An Efficient Phishing Website Detection Plugin Service Based on Website Trustworthiness Evaluation," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(8), pages 3569-3580, August.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-8:p:3569-3580
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