Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails

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

Text (© the Authors)
asi-05-00073.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (3MB) | Preview


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

University Staff: Request a correction | Repository Editors: Update this record

University Of Gloucestershire

Bookmark and Share

Find Us On Social Media:

Social Media Icons Facebook Twitter Google+ YouTube Pinterest Linkedin

Other University Web Sites

University of Gloucestershire, The Park, Cheltenham, Gloucestershire, GL50 2RH. Telephone +44 (0)844 8010001.