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Cláudio Marques1, Silvestre Malta2, João Paulo Magalhães3

  • 1Escola Superior de Tecnologia e Gestão, Politécnico de Viana do Castelo, Viana do Castelo 4900-348, Portugal.

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Machine learning can identify malicious domain names that traditional block lists miss. This study introduces a balanced dataset of 90,000 malicious and non-malicious domain names for supervised learning, enhancing internet security.

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • The Domain Name Service (DNS) is critical for internet functionality, with malicious actors exploiting domain names for illicit activities.
  • Current DNS firewall solutions primarily rely on updated lists of known malicious domains, leaving unknown threats unaddressed.
  • Machine learning offers a promising approach to detect previously unknown malicious domain names, improving cybersecurity defenses.

Purpose of the Study:

  • To develop a comprehensive dataset for supervised machine learning models aimed at classifying malicious versus non-malicious domain names.
  • To facilitate the detection of novel and evolving cyber threats that bypass traditional security measures.

Main Methods:

  • A novel dataset was constructed using publicly available DNS logs, encompassing both malicious and non-malicious domain names.
  • 34 distinct features were extracted from each domain name, including entropy, character patterns, length, creation date, IP information, open ports, and geolocation via OSINT.
  • The dataset was balanced, comprising approximately 90,000 domain names with an equal 50/50 split between malicious and non-malicious classifications.

Main Results:

  • The created dataset provides a robust foundation for training and evaluating machine learning models for domain name classification.
  • The feature engineering approach combines direct domain name analysis with external data enrichment for comprehensive analysis.
  • The balanced nature of the dataset ensures fair model training and reduces bias in classification.

Conclusions:

  • The developed dataset is crucial for advancing machine learning-based detection of malicious domain names.
  • This resource aids in building more effective DNS security solutions capable of identifying zero-day threats.
  • The study contributes to enhancing overall internet security by enabling proactive identification of malicious online activities.