CompPhish

Published: 24 August 2026| Version 4 | DOI: 10.17632/fmbs4kp9wz.4
Contributor:
Richa Goenka

Description

About the dataset : This is the version 4 of a comprehensive phishing dataset which includes labelled phishing as well as legitimate URLs along with their respective HTML codes. Each URL and its HTML code file is associated with the same serial number. The dataset size is 15,358 samples, where 7,204 samples are phishing, and 8,154 are legitimate. Data Collection: Phishing URLs are collected from PhishTank and OpenPhish repositories and legitimate URLs from the DataForSEO Top-1000 websites list. The HTML codes of the URLs are downloaded by using the Python Programming Language after visiting the URL while it is active. Data collection period is from September 2024 to August 2025. Label Information: Labels 0 for legitimate and 1 for phishing are used. Information about Features: 70 features are extracted from the raw URLs and their HTML codes. These features cover various types of phishing attacks: URL-based phishing attacks, brand-jacking, phishing sites hosted on compromised domains (PSHCD), and auto-downloadable malicious files links. Usage: The processed dataset can be used by researchers for further analysis by applying various ML algorithms or feature selection techniques to achieve considerable results. The raw URLs and their HTML source code can also be used for extracting novel features and proposing novel detection methodologies. This version presents additional files to improve the reproducibility for the users (requirements.txt for python library versions, updated Data Dictionary for computational rules/ Thresholds/criteria and example values, additional auxiliary files that are used for computation of some features). README file gives a description of each file shared on this repository.

Files

Steps to reproduce

Explained in the Readme.docx file. The data dictionary included with the dataset gives a clear description of each feature including the data type, computational rules/ threshold/criteria and example values for a sample URL.

Institutions

Categories

Cybersecurity, Machine Learning, Information Security

Licence