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The general need exists for robust threat detection methods that adapt to new and evolving cyber threats. In this paper, we propose a method that uses RDF-based knowledge graphs and AI to improve detection and classification of attacks. Our approach transforms security logs into RDF and maps them to the MITRE ATT&CK framework. We also address sensitive data concerns, such as IP addresses, by applying privacy measures. Our results show that AI-driven analysis and knowledge graphs can enhance cyber threat intelligence (CTI), while respecting privacy regulations.

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Integrating AI and RDF Knowledge Graphs with MITRE ATT&CK for CTI Insights

  • 会议论文

  • 发表日期：

    2026年

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  • Apostolos Mavridis,
  • Stergios Tegos,
  • Christos Anastasiou,
  • Maria Papoutsoglou,
  • Georgios Meditskos

摘要

The general need exists for robust threat detection methods that adapt to new and evolving cyber threats. In this paper, we propose a method that uses RDF-based knowledge graphs and AI to improve detection and classification of attacks. Our approach transforms security logs into RDF and maps them to the MITRE ATT&CK framework. We also address sensitive data concerns, such as IP addresses, by applying privacy measures. Our results show that AI-driven analysis and knowledge graphs can enhance cyber threat intelligence (CTI), while respecting privacy regulations.