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Proactive Phishing Defense: A URL Classification System Using Machine Learning
ID：80 View Protection：ATTENDEE Updated Time：2024-10-12 10:03:13 Hits：558 Virtual Presentation

Start Time：2024-10-26 10:20（Asia/Bangkok）

Duration：15min

Session：RS1 Regular Session 1 » RS1-3Emerging 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
Researcher Aliraqia University

Submission Author
Samer Jawad Aliraqia University
Satea Alnajjar Aliraqia University
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Important Date

  • Conference Date

    Oct 24

    2024

    to

    Oct 27

    2024

  • Oct 14 2024

    Draft paper submission deadline

  • Oct 29 2024

    Registration deadline

  • Oct 31 2024

    Contribution 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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