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Updated: Dec 25, 2025

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UMUDGA: A dataset for profiling algorithmically generated domain names in botnet detection.

Mattia Zago1, Manuel Gil Pérez1, Gregorio Martínez Pérez1

  • 1Department of Information Engineering and Communications, University of Murcia, Campus Espinardo Murcia 30100 Spain.

Data in Brief
|March 28, 2020
PubMed
Summary
This summary is machine-generated.

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This study introduces a large dataset of over 30 million domain names to improve the detection of botnets using machine learning. The dataset aids researchers in developing advanced cybersecurity solutions against domain generation algorithms (DGAs).

Area of Science:

  • Computer Security
  • Cybersecurity
  • Machine Learning

Background:

  • Botnets pose a significant cyber threat, employing sophisticated techniques like dynamic addressing and domain generation algorithms (DGAs) for concealment.
  • Existing detection methods require enhancement to effectively counter these evolving threats.

Purpose of the Study:

  • To present a comprehensive dataset of manually-labeled, algorithmically generated domain names for machine learning analysis.
  • To facilitate research in developing more effective botnet detection systems.

Main Methods:

  • Generation of over 30 million domain names using DGAs from 50 notorious malware variants in a controlled environment.
  • Extraction of a feature set comprising statistical and natural language processing metrics for each domain.
Keywords:
DataDomain Generation Algorithm (DGA)Machine learningNatural Language Processing (NLP)Network security

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  • Manual labeling of all generated domain names.

    • Main Results:

    • A ready-to-use dataset for machine learning analysis, including both raw domain lists and extracted features.
    • The dataset enables researchers to bypass data collection and pre-processing, focusing on ML-powered detection.

    Conclusions:

    • The presented dataset significantly advances the field by providing a robust resource for DGA-based botnet detection research.
    • It empowers the development of improved network intrusion detection systems through focused ML analysis.