Application of Machine Learning for Real-Time Phishing Attack Detection - Akshay Shankar Agrawal; Sanketi Raut; Andrina Dsouza; Jimit Mehta; Prajwal Naik (ITEGAM-JETIA)
Over the years, the Internet has been exploited to carry out a range of cyber attacks, with phishing being the most prominent one. Increasingly sophisticated techniques of phishing have threatened the security of many Internet-based systems. To be able to detect suspicious websites is a potential first step in reducing the amount of phishing attacks occurring daily. This paper outlines the development and implementation of a platform to detect phishing websites. It highlights the pressing need for early detection of possible phishing attacks to prevent data theft, frauds, etc. The system uses machine learning algorithms to distinguish legitimate websites from phishing websites and generate a prediction to be used for the platform. A user interface is implemented to have two parts. The first part includes a text field for entering a URL, which the ML model processes to give a prediction that gets displayed to the user. Another module gathers URLs as they arrive from an API and scans them for potentially suspicious websites. The final ML model, a Random Forest classifier with 27 estimators, had an accuracy of 96.12% and F1 score of 95.94%. Future enhancements and research directions are also discussed for further development of the system.