A Sophisticated Framework for the Accurate Detection of Phishing Websites

Phishing is an increasingly sophisticated form of cyberattack that is inflicting huge financial damage to corporations throughout the globe while also jeopardizing individuals' privacy. Attackers are constantly devising new methods of launching such assaults and detecting them has become a daunting task. Many different techniques have been suggested, each with its own pros and cons. While machine learning-based techniques have been most successful in identifying such attacks, they continue to fall short in terms of performance and generalizability. This paper proposes a comprehensive methodology for detecting phishing websites. The goal is to design a system that is capable of accurately distinguishing phishing websites from legitimate ones and provides generalized performance over a broad variety of datasets. A combination of feature selection, greedy algorithm, cross-validation, and deep learning methods have been utilized to construct a sophisticated stacking ensemble classifier. Extensive experimentation on four different phishing datasets was conducted to evaluate the performance of the proposed technique. The proposed algorithm outperformed the other existing phishing detection models obtaining accuracy of 97.49%, 98.23%, 97.48%, and 98.20% on dataset-1 (UCI Phishing Websites Dataset), dataset-2 (Phishing Dataset for Machine Learning: Feature Evaluation), dataset-3 (Phishing Websites Dataset), and dataset-4 (Web page phishing detection), respectively. The high accuracy values obtained across all datasets imply the models' generalizability and effectiveness in the accurate identification of phishing websites.