A multi-dimensional DNS domain intelligence dataset for cybersecurity research

Area of Science:

• Cybersecurity and Network Security
• Data Science and Machine Learning

Background:

• Cyber threats like phishing and malware are increasing in sophistication and frequency.
• Data-driven approaches are crucial for effective detection and analysis of these threats.
• Existing datasets lack the comprehensive, multi-dimensional data needed for robust cybersecurity research.

Purpose of the Study:

• To present a unique, large-scale dataset for network security research.
• To facilitate the classification and analysis of internet domains for threat identification.
• To provide a rich resource for developing and testing advanced cybersecurity solutions.

Main Methods:

• Collected data from over a million internet domains.
• Integrated metainformation from diverse sources: DNS, TLS, WHOIS, RDAP, IP, and geolocation.
• Formatted the dataset in JSON for broad accessibility and integration.

Main Results:

• The dataset contains detailed labels distinguishing phishing, malware, and benign internet domains.
• It offers a comprehensive, multi-dimensional view of domain characteristics.
• The dataset's volume and feature variety exceed current public resources.

Conclusions:

• The presented dataset is an invaluable asset for advancing cybersecurity research.
• Its rich, multi-dimensional data supports deeper analysis of cyber threats.
• The dataset facilitates the development of more robust and adaptable cybersecurity solutions.