# Structuring Security: A Survey of Cybersecurity Ontologies, Semantic Log Processing, and LLMs Application

arXiv: https://arxiv.org/abs/2510.16610 · HTML: https://mlsanity.com/paper/2510.16610/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2025.

## Built on by (most cited first, 3 of 3)

- [Supporting Cybersecurity Risk Management for Medical Devices via the SECUMAN Ontology and Shapes](https://mlsanity.com/paper/2608.00698/) (2026)
- [Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Tasks](https://mlsanity.com/paper/2608.05867/) (2026)
- [NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing](https://mlsanity.com/paper/2609.00604/) (2026)

## 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.