Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection
Abstract
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.
Cite
@article{arxiv.2405.11619,
title = {Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection},
author = {Abdulla Al-Subaiey and Mohammed Al-Thani and Naser Abdullah Alam and Kaniz Fatema Antora and Amith Khandakar and SM Ashfaq Uz Zaman},
journal= {arXiv preprint arXiv:2405.11619},
year = {2026}
}
Comments
19 pages, 7 figures, dataset link: https://www.kaggle.com/datasets/naserabdullahalam/phishing-email-dataset/