# MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detection

arXiv: https://arxiv.org/abs/2507.17978 · HTML: https://mlsanity.com/paper/2507.17978/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 23 Jul 2025.

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

- [ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production](https://mlsanity.com/paper/2505.09999/) (2025)
- [ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection](https://mlsanity.com/paper/2402.18093/) (2024)
- [Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection](https://mlsanity.com/paper/2405.11619/) (2024)
- [Enhancing Phishing Email Identification with Large Language Models](https://mlsanity.com/paper/2502.04759/) (2025)
- [Using Lexical Features for Malicious URL Detection -- A Machine Learning Approach](https://mlsanity.com/paper/1910.06277/) (2019)
- [An intelligent classification model for phishing email detection](https://mlsanity.com/paper/1608.02196/) (2016)
- [MEADE: Towards a Malicious Email Attachment Detection Engine](https://mlsanity.com/paper/1804.08162/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Hybrid, Multi-Layered Pipeline for Phishing and Threat Classification: Independently Validated URL and NLP Engines with a Calibrated Multi-Channel Fusion Stage](https://mlsanity.com/paper/2606.21690/) (2026)

## Abstract (arXiv)

Phishing emails continue to pose a significant threat to cybersecurity by exploiting human vulnerabilities through deceptive content and malicious payloads. While Machine Learning (ML) models are effective at detecting phishing threats, their performance largely relies on the quality and diversity of the training data. This paper presents MeAJOR (Merged email Assets from Joint Open-source Repositories) Corpus, a novel, multi-source phishing email dataset designed to overcome critical limitations in existing resources. It integrates 135894 samples representing a broad number of phishing tactics and legitimate emails, with a wide spectrum of engineered features. We evaluated the dataset's utility for phishing detection research through systematic experiments with four classification models (RF, XGB, MLP, and CNN) across multiple feature configurations. Results highlight the dataset's effectiveness, achieving 98.34% F1 with XGB. By integrating broad features from multiple categories, our dataset provides a reusable and consistent resource, while addressing common challenges like class imbalance, generalisability and reproducibility.