A Sophisticated Framework for the Accurate Detection of Phishing Websites
Asif Newaz, Farhan Shahriyar Haq, Nadim Ahmed

TL;DR
This paper introduces a sophisticated ensemble machine learning framework that significantly improves the accuracy and generalizability of phishing website detection across multiple datasets.
Contribution
It presents a novel stacking ensemble classifier combining feature selection, greedy algorithms, and deep learning for enhanced phishing detection performance.
Findings
Achieved over 97% accuracy on four diverse datasets.
Outperformed existing phishing detection models.
Demonstrated high generalizability across datasets.
Abstract
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…
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Taxonomy
TopicsSpam and Phishing Detection · Misinformation and Its Impacts · Text and Document Classification Technologies
