Analysing the email data using stylometric method and deep learning to mitigate phishing attack

Wosah, Peace Nmachi, Ali Mirza, Qublai and Sayers, William ORCID: 0000-0003-1677-4409 (2024) Analysing the email data using stylometric method and deep learning to mitigate phishing attack. International Journal of Information Technology. doi:10.1007/s41870-024-01839-5 (In Press)

Text 14124 Wosah, Peace Nmachi et al (2024) ANALYSING THE EMAIL DATA USING STYLOMETRIC METHOD AND DEEP LEARNING TO MITIGATE PHISHING ATTACK.pdf - Accepted Version Restricted to Repository staff only until 5 May 2025. (Publisher Embargo). Available under License Publisher's Licence. Download (901kB)

Abstract

The high-volume usage of email has attracted cybercriminals to the platform and criminals are aware of difficulties users often have in separating legitimate from illegitimate emails and seek to take advantage of those difficulties by impersonating staff of a trusted organisation to persuade users into divulging their private information. To help users overcome the difficulty in detecting phishing attacks, a system is proposed. Recent advancement uses: stylometric features, gender features and personality features to carry out a sender verification process. The existing approaches are more complex and if the system fails to detect bad email, and it gets to users, the possibility of becoming a victim becomes high if not detected by the user. The proposed framework adds Colour Code to Email Verification (CCEV). It conducts sender’s verification at the recipients’ end based on 3-features related with senders, writing pattern, gender, and header.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Email data; Phishing attack; Spear-phishing; Mitigation; Sender verification; Colour Code
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management > HD61 Risk in industry. Risk management Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Kamila Niekoraniec
Date Deposited:	19 Jun 2024 14:20
Last Modified:	11 Jul 2024 08:00
URI:	https://eprints.glos.ac.uk/id/eprint/14124

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