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Real-time Malicious URL Detection Using Machine Learning

Suraj Prabhu, Sushant Pagam, Adarsh Pednekar, Sujata Bhairnallykar


In malicious URLs (universal resource locators)discovery, antiquated classifiers are tested because of the information volume is enormous, designs are dynamical over the long haul, and furthermore the relationships among choices are modern. Feature engineering plays a crucial role in addressing these issues. To raised representation, the underlying the drawback and improve the performances of classifiers in characteristic malicious URLs.This paper proposes a mixture of linear and non-linear house transformation strategies. This paper proposes machine learning based mostly on the approach to scrutinizing universal resource locator instead of linear approaching of saving information and examination 33,1622 URLs with 62 options were collected to validate the planned feature engineering strategies.


URL detection, natural language processing, machine learning, uniform resources, downstream algorithms.

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