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Phishing attacks are the most common type of cyber-attacks used to obtainsensitive information and have been affecting individuals as well asorganisations across the globe. Various techniques have been proposed toidentify the phishing attacks specifically, deployment of machine intelligencein recent years. However, the deployed algorithms and discriminating factorsare very diverse in existing works. In this study, we present a comprehensiveanalysis of various machine learning algorithms to evaluate their performancesover multiple datasets. We further investigate the most significant featureswithin multiple datasets and compare the classification performance with thereduced dimensional datasets. The statistical results indicate that randomforest and artificial neural network outperform other classificationalgorithms, achieving over 97% accuracy using the identified features.

Phishing Attacks and Websites Classification Using Machine Learning and Multiple Datasets (A Comparative Analysis)