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

arXiv: https://arxiv.org/abs/2405.11619 · HTML: https://mlsanity.com/paper/2405.11619/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 19 May 2024.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Evaluation of Federated Learning in Phishing Email Detection](https://mlsanity.com/paper/2007.13300/) (2020)
- [Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)](https://mlsanity.com/paper/1908.03640/) (2019)
- [Deep Learning: Computational Aspects](https://mlsanity.com/paper/1808.08618/) (2018)
- [Phishing Detection Leveraging Machine Learning and Deep Learning: A Review](https://mlsanity.com/paper/2205.07411/) (2022)

## Built on by (most cited first, 7 of 7)

- [Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements](https://mlsanity.com/paper/2502.12904/) (2025)
- [Private Synthetic Text Generation with Diffusion Models](https://mlsanity.com/paper/2410.22971/) (2024)
- [E-PhishGen: Unlocking Novel Research in Phishing Email Detection](https://mlsanity.com/paper/2509.01791/) (2025)
- [MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detection](https://mlsanity.com/paper/2507.17978/) (2025)
- [Dual-Path Phishing Detection: Integrating Transformer-Based NLP with Structural URL Analysis](https://mlsanity.com/paper/2509.20972/) (2025)
- [A Robust and Explainable Transformer-Based Framework for Phishing Email Detection](https://mlsanity.com/paper/2511.12085/) (2025)
- [PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents](https://mlsanity.com/paper/2603.11955/) (2026)

## Abstract (arXiv)

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.