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

ABSTRACT: Phishing attacks are the most common type of cyber-attacks used to obtain
sensitive information and have been affecting individuals as well as
organisations across the globe. Various techniques have been proposed to
identify the phishing attacks specifically, deployment of machine intelligence
in recent years. However, the deployed algorithms and discriminating factors
are very diverse in existing works. In this study, we present a comprehensive
analysis of various machine learning algorithms to evaluate their performances
over multiple datasets. We further investigate the most significant features
within multiple datasets and compare the classification performance with the
reduced dimensional datasets. The statistical results indicate that random
forest and artificial neural network outperform other classification
algorithms, achieving over 97% accuracy using the identified features.
Phishing attacks are the most common type of cyber-attacks used to obtain
sensitive information and have been affecting individuals as well as
organisations across the globe. Various techniques have been proposed to
identify the phishing attacks specifically, deployment of machine intelligence
in recent years. However, the deployed algorithms and discriminating factors
are very diverse in existing works. In this study, we present a comprehensive
analysis of various machine learning algorithms to evaluate their performances
over multiple datasets. We further investigate the most significant features
within multiple datasets and compare the classification performance with the
reduced dimensional datasets. The statistical results indicate that random
forest and artificial neural network outperform other classification
algorithms, achieving over 97% accuracy using the identified features.
Phishing Attacks and Websites Classification Using Machine Learning and Multiple Datasets (A Comparative Analysis)
http://arxiv.org/abs/2101.02552v1