Efficient Machine Learning for Prediction of Malicious URLs under Neutrosophic Uncertainty Framework

Efficient Machine Learning for Prediction of Malicious URLs under Neutrosophic Uncertainty Framework

With more than 5.44 billion users, the Internet is an essential component of everyday life, facilitating e-commerce, interaction, learning, and more. But with the proliferation of harmful Uniform Resource Locators (URLs), this widespread Internet access also raises questions about online security an...

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Bibliographic Details
Main Authors: Mohamed Eassa, Ahmed Abdelhafeez, Ahmed A. Metwaly, Ahmed S. Salama
Format: Article
Language:English
Published: University of New Mexico 2025-05-01
Series:Neutrosophic Sets and Systems
Subjects:
prediction of malicious urls
neutrosophic sets
uncertainty
machine learning
security
Online Access:https://fs.unm.edu/NSS/26MaliciousURLs.pdf
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https://fs.unm.edu/NSS/26MaliciousURLs.pdf

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