Structuring Security: A Survey of Cybersecurity Ontologies, Semantic Log Processing, and LLMs Application
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Abstract (arXiv)
This survey investigates how ontologies, semantic log processing, and Large Language Models (LLMs) enhance cybersecurity. Ontologies structure domain knowledge, enabling interoperability, data integration, and advanced threat analysis. Security logs, though critical, are often unstructured and complex. To address this, automated construction of Knowledge Graphs (KGs) from raw logs is emerging as a key strategy for organizing and reasoning over security data. LLMs enrich this process by providing contextual understanding and extracting insights from unstructured content. This work aligns with European Union (EU) efforts such as NIS 2 and the Cybersecurity Taxonomy, highlighting challenges and opportunities in intelligent ontology-driven cyber defense.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2025.
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Citing papers per year: 2026: 3.
3 papers.
- 2026Supporting Cybersecurity Risk Management for Medical Devices via the SECUMAN Ontology and Shapescited by 0
- 2026Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Taskscited by 0
- 2026NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturingcited by 0
- 2026NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing1 Sep 2026
- 2026Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Tasks6 Aug 2026
- 2026Supporting Cybersecurity Risk Management for Medical Devices via the SECUMAN Ontology and Shapes1 Aug 2026