Malicious Websites Detection Using Machine Learning

  • J. T. Anita Rose, Jesline Daniel, F. Sangeetha Francelin Vinnarasi, S. Yogeswaran, N. Sandeep


In this ever-evolving technological world, more users are rapidly accessing the internet through browsers without knowing how it actually works. Knowing this fact, some anonymous people steal their personal information and other important credentials without their consent for illegal use. These people commonly known as hackers, use different methods to trick the users into falling for their traps. Some of the methods include malicious websites, emails, and links that redirect the user’s data to the hackers. These websites are called phishing websites and are primarily used to gain sensitive information such as credit or debit card details, usernames and passwords. Our proposed solution includes a warning system that acts as an interface between the user and website, intimating the user about these malicious websites. This system uses machine learning algorithms to identify the websites that are harmful based on some basic website attributes such as URL, the content of the website, IP address, etc. Furthermore, it also restricts the website’s access to the user’s resources by intercepting all types of data transfer in between them. Thus, the system helps in decreasing the user vulnerability to malicious websites and therefore enhancing the internet security.

Keywords:   Dataset, Machine learning-Classification method, python, malicious websites etc.

How to Cite
J. T. Anita Rose, Jesline Daniel, F. Sangeetha Francelin Vinnarasi, S. Yogeswaran, N. Sandeep. (2020). Malicious Websites Detection Using Machine Learning. International Journal of Advanced Science and Technology, 29(3), 9328 - 9344. Retrieved from