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Dewis, M and Viana, Thiago 
ORCID: 0000-0001-9380-4611
(2022)
Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails.
Applied System Innovation, 5 (4).
e73.
doi:10.3390/asi5040073
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Text (© the Authors)
asi-05-00073.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (3MB) | Preview |
Abstract
Using technology to prevent cyber-attacks has allowed organisations to somewhat automate cyber security. Despite solutions to aid organisations, many are susceptible to phishing and spam emails which can make an unwanted impact if not mitigated. Traits that make organisations susceptible to phishing and spam emails include a lack of awareness around the identification of malicious emails, explicit trust, and the lack of basic security controls. For any organisation, phishing and spam emails can be received and the consequences of an attack could result in disruption. This research investigated the threat of phishing and spam and developed a detection solution to address this challenge. Deep learning and natural language processing are two techniques that have been employed in related research, which has illustrated improvements in the detection of phishing. Therefore, this research contributes by developing Phish Responder, a solution that uses a hybrid machine learning approach combining natural language processing to detect phishing and spam emails. To ensure its efficiency, Phish Responder was subjected to an experiment in which it has achieved an average accuracy of 99% with the LSTM model for text-based datasets. Furthermore, Phish Responder has presented an average accuracy of 94% with the MLP model for numerical-based datasets. Phish Responder was evaluated by comparing it with other solutions and through an independent t-test which demonstrated that the numerical-based technique is statistically significantly better than existing approaches.
| Item Type: | Article |
|---|---|
| Article Type: | Article |
| Uncontrolled Keywords: | phishing; spam; deep learning; machine learning; natural language processing |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Research Priority Areas: | Applied Business & Technology |
| Depositing User: | Thiago Viana |
| Date Deposited: | 01 Aug 2022 15:04 |
| Last Modified: | 31 Oct 2023 11:18 |
| URI: | https://eprints.glos.ac.uk/id/eprint/11406 |
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