Phishing email detection using Natural Language Processing techniques : a literature survey

Salloum, S ORCID: https://orcid.org/0000-0002-6073-3981, Gaber, TMA ORCID: https://orcid.org/0000-0003-4065-4191, Vadera, S ORCID: https://orcid.org/0000-0001-6041-2646 and Shaalan, K 2021, Phishing email detection using Natural Language Processing techniques : a literature survey , in: ACLing 2021: 5th International Conference on AI in Computational Linguistics, 4th-5th June 2021, Online.

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Abstract

Phishing is the most prevalent method of cybercrime that convinces people to provide sensitive information; for instance, account IDs, passwords, and bank details. Emails, instant messages, and phone calls are widely used to launch such cyber-attacks. Despite constant updating of the methods of avoiding such cyber-attacks, the ultimate outcome is currently inadequate. On the other hand, phishing emails have increased exponentially in recent years, which suggests a need for more effective and advanced methods to counter them. Numerous methods have been established to filter phishing emails, but the problem still needs a complete solution. To the best of our knowledge, this is the first survey that focuses on using Natural Language Processing (NLP) and Machine Learning (ML) techniques to detect phishing emails. This study provides an analysis of the numerous state-of-the-art NLP strategies currently in use to identify phishing emails at various stages of the attack, with an emphasis on ML strategies. These approaches are subjected to a comparative assessment and analysis. This gives a sense of the problem, its immediate solution space, and the expected future research directions.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Procedia Computer Science
Publisher: Elsevier
ISSN: 1877-0509
Related URLs:
Depositing User: USIR Admin
Date Deposited: 02 Sep 2021 09:17
Last Modified: 02 Sep 2021 09:17
URI: http://usir.salford.ac.uk/id/eprint/61722

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