  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.
