Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection

2024/05/19 by Abdulla Al-Subaiey, Al-Subaiey, Abdulla, Mohammed Al-Thani +9 · 4 citations
Computer Science · Neuroscience · #Artificial Intelligence (cs.AI) #Brain Tumor Detection and Classification #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Spam and Phishing Detection

paper · pdf · doi:10.48550/arxiv.2405.11619

openalex publication_date 2024/05/19 · openalex created_date 2024/05/22 · openalex updated_date 2026/07/28

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.

Cited by

• A Robust and Explainable Transformer-Based Framework for Phishing Email Detection
• Dual-Path Phishing Detection: Integrating Transformer-Based NLP with Structural URL Analysis
• E-PhishGen: Unlocking Novel Research in Phishing Email Detection
• MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detection

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