Guardian Shield: A Machine Learning Approach for Proactive Malicious URL Detection

Journal: GRENZE International Journal of Engineering and Technology
Authors: Anshika Singh, Shagun Chaudhary, Prabhjot Kaur
Volume: 10 Issue: 2
Grenze ID: 01.GIJET.10.2.208 Pages: 4118-4121

Abstract

In today’s time we can see that everything is depend on the Information superhighway means that many people use their instant on the web. India is also known as a Digital India. Many of the activities conducted online, therefore here is a possibility to make the data corrupt or add any miscellaneous activity to hack our information. Hackers try to hack that data which is generally used by us like education websites, shopping websites etc. they add or do minor change in the URL of any website to hack our data which is harm for us, that’s why we are going to implement that type of model which will help to detect any miscellaneous URL of any websites. We will use ML algorithms like Random Forest, XG Boost, Light GBM model to easily detection. As a result, these ML algorithms give us the best accuracy.

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