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

Khan, S, Khan, W and Hussain, A Phishing Attacks and Websites Classification Using Machine Learning and Multiple Datasets (A Comparative Analysis). In: ICIC, 2020 International Conference on Intelligent Computing, 02 October 2020 - 05 October 2020, Italy. (Accepted)

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Abstract

Phishing attacks are the most common type of cyber-attacks used to obtain sensitive information and have been affecting individuals as well as or-ganizations 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 di-verse 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 da-tasets 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% accu-racy using the identified features.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Phishing Attacks, Cyber Security, Phishing Emails, Information Security, Security and Privacy, Phishing Classification, Artificial Intelligence, Phishing Websites Detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Date Deposited: 26 Aug 2020 15:17
Last Modified: 26 Aug 2020 15:17
URI: http://researchonline.ljmu.ac.uk/id/eprint/13554

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