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ABSTRACT LIBRARY

Proactive Phishing Defense: A URL Classification System Using Machine Learning

Publisher: USS

Authors: Jawad Samer, Aliraqia University Alnajjar Satea, Aliraqia University

Open Access

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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.

Published in: IEEE Transactions on Antennas and Propagation( Volume: 71, Issue: 4, April 2023)

Page(s): 2908 - 2921

Date of Publication: 2908 - 2921

DOI: 10.1109/TAP.2023.3240032

Publisher: UNITED SOCIETIES OF SCIENCE