Novel interpretable and robust web-based AI platform for phishing email detection

被引：1

作者：

Al-Subaiey, Abdulla ^{[1
]}

Al-Thani, Mohammed ^{[1
]}

Alam, Naser Abdullah ^{[2
]}

Antora, Kaniz Fatema ^{[2
]}

Khandakar, Amith ^{[3
]}

Zaman, S. M. Ashfaq Uz ^{[4
]}

机构：

[1] AFG Coll Univ Aberdeen, Dept Comp Sci, Doha, Qatar

[2] Univ Liberal Arts Bangladesh, Dept Comp Sci & Engn, Dhaka, Bangladesh

[3] Qatar Univ, Coll Engn, Dept Elect Engn, Doha, Qatar

[4] Qatar Emiri Naval Forces, POB 2237, Doha, Qatar

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2024年 / 120卷

关键词：

Phishing emails; Machine learning model; Email classification; Dataset; Explainable AI; User trust; Web-based application; SPAM EMAIL; MODEL;

D O I：

10.1016/j.compeleceng.2024.109625

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：23

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