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

TL;DR

The MeAJOR Corpus is a comprehensive, multi-source dataset of over 135,000 phishing and legitimate emails designed to improve machine learning detection models by providing diverse, high-quality training data that enhances generalizability and reproducibility.

Contribution

This paper introduces the MeAJOR Corpus, a large, multi-source phishing email dataset with engineered features, addressing limitations of existing datasets and supporting advanced detection research.

Findings

Achieved 98.34% F1 score with XGB classifier.

Demonstrated dataset's effectiveness across multiple models.

Addressed class imbalance and reproducibility challenges.

Abstract

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.