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Proactive Phishing Defense: A URL Classification System Using Machine Learning
ID：80 View protection：Participant Only Updated time：2024-10-12 10:03:13 Views：508 Virtual Presentation

Start Time：2024-10-26 10:20

Duration：15min

Session：[RS1] Regular Session 1 [RS1-3] Emerging Trends of AI/ML

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Abstract
Phishing attacks are the most common cyber attacks nowadays. Phishing attacks rely on social engineering concepts. However, URLs are a fulcrum for phishing attacks. A web application is proposed to classify URLs based on the Random Forest model, and results with an accuracy of 98.2% are achieved.
Keywords
Decision trees, Feature extraction, Phishing, Random Forest, URLs.
Speaker
Samer Jawad
Aliraqia University

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Important Dates

  • Conference date

    10-24

    2024

    -

    10-27

    2024

  • 10-14 2024

    Draft paper submission deadline

  • 10-29 2024

    Registration deadline

  • 10-31 2024

    Presentation submission deadline

Sponsored By

United Societies of Science
King Mongkut's University of Technology North Bangkok (KMUTNB)
IEEE Thailand Section
IEEE Thailand Section C Chapter

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