Novel interpretable and robust web-based AI platform for phishing email detection
Phishing emails continue to pose a significant threat, causing financial losses and security breaches. This study addresses limitations in existing research, such as reliance on proprietary datasets and lack of real-world application, by proposing a high-performance machine learning model for email classification. Utilizing a comprehensive and largest available public dataset, the model achieves a f1 score of 0.99 and is designed for deployment within relevant applications. Additionally, Explainable AI (XAI) is integrated to enhance user trust. This research offers a practical and highly accurate solution, contributing to the fight against phishing by empowering users with a real-time web-based application for phishing email detection.
Authors
- Amith Abdullah Khandakar (ORCID: https://orcid.org/0000-0001-7068-9112)
- SM Ashfaq Uz Zaman
- Naser Abdullah Alam (ORCID: https://orcid.org/0000-0002-8086-7223)
- Abdulla Al-Subaiey
- Mohammed Al-Thani
- Kaniz Fatema Antora
Institutions
- University of Liberal Arts Bangladesh (BD)
- Gulf English School (QA)
- Qatar University (QA)
Publication Details
- Journal
- Computers & Electrical Engineering
- Published
- 2024-09-10
- DOI
- https://doi.org/10.1016/j.compeleceng.2024.109625
- Citations
- 25
- Primary Topic
- Spam and Phishing Detection
- Type
- article
- Field-Weighted Citation Impact
- 19.48