DETECTING PHISHING WEBSITES WITH AN ENSEMBLE MACHINE LEARNING METHOD

Abstract: phishing websites now pose a critical threat to digital infrastructure across industries. They frequently serve as the initial vector for various cyber intrusions that steal, change, or gain access to both customer and company data. This study presents a new way to find phishing websites by combining attribute selection and data point derivation methods with an ensemble-based machine learning algorithm. It does this after looking at all the research that is already out there. The proposed method uses a carefully chosen dataset to build and test ensemble models that can accurately predict phishing activity.