Browser Extension for Malicious URL Detection Based on Machine Learning Model

In the aftermath of the technological revolution, individuals started to depend more and more on the internet as their primary method of communication. In today's world, people use this platform for a variety of activities, including shopping, watching movies, engaging with friends on social media, getting information, and even learning. Malicious URLs are the most common source of danger in today's digital environment, according to security experts. For example, targeting people, pushing schemes, and perpetrating fraud are only a few examples of what is prohibited under the law. Malware is mostly targeted at email, with 92 percent of the 50,000 security events observed being directed towards email (Sanders, 2021). Traditionally, this recognition has been performed mostly through the use of blacklists. Blacklists, on the other hand, are not comprehensive and are unable to discover newly formed harmful URLs. In recent years, machine learning approaches have garnered more attention as a way of enhancing the generality of malicious URL detectors, which is an important goal. The purpose of the project is to provide a machine learning model with good accuracy that can be used on both the client-side and the application-side. The service will be available as a browser extension and an API, respectively.