Machine Learning-Driven Detection of Malicious Websites for Consumer Protection
Arun Kumar Dubey, Achin Jain, Sarjana Singh, Sudhakar Kumar, Vanita Jain, Varsha Arya, Kwok Tai Chui, Brij Bhooshan Gupta · 2025
The Internet has seen significant improvements in the number and type of web over the past few years. Resources such as online banking, gambling, and social networking sites have improved. As a result, large amounts of data are uploaded to the Internet daily. However, the Internet can be used as a tool for various criminal activities such as financial fraud, spam, or commercial email or can be used to misuse personal data. All of these things happen because of harmful websites. Innocent users working in browsers have no idea what is happening at the end of the page. These users may be tricked into providing their sensitive information, or they may download harmful data. To counteract this, the project works by looking at various ways to identify malicious websites with the help of different machine learning algorithms. We have used URL opening methods that process bad URLs