Authors: Ms. Manepalli Kavya, Mrs. Jitendar Ahuja
Abstract: Recent years have seen a significant increase in phishing attacks targeting websites, posing persistent challenges to digital security. While numerous detection tools have been developed, they often fall short in comprehensively identifying all threats and struggle with subtle, evolving forms of deception. Integrating machine learning (ML) techniques has emerged as the most effective strategy to overcome these limitations, significantly enhancing detection accuracy and computational efficiency. This approach is crucial for addressing the shortcomings of existing phishing detection models. This paper introduces an Intelligent Phishing defence paradigm, leveraging an ENASSEMBLE-driven ML model specifically trained on a designed dataset for high-fidelity website identification. Our objective is to demonstrate how the ENASSEMBLE model not only bolsters the overall accuracy of phishing detection but also offers a robust and efficient solution capable of recognizing complex and evasive fraudulent sites, thereby fortifying online security.
DOI: https://doi.org/10.5281/zenodo.15790035
