Authors - Yasmine AGOUN, Cheikh SALMI, Nour El-Houda SENOUSSI Abstract - The rapid growth of the Internet of Things (IoT) has made cybersecurity prone to several vulnerabilities, highlighting the need for a semantic and well-organized structure for cybersecurity knowledge to ensure reliable threat detection and mitigation. In this paper, we propose IoTSecOnto, a largely automated pipeline for building a security-centric Internet of Things (IoT) ontology. The pipeline combines automated security literature mining, text analytics, natural language processing, Large Language Models (LLMs), and formal concept analysis. This approach reveals domain-specific concepts and relations and organizes them into a coherent hierarchy. A human-assisted review phase is needed to ensure the reliability and the accuracy of the derived security knowledge. In addition, the ontology is designed to be flexible and can be improved over time. IoTSecOnto is implemented using OWL 2, RDFLib, SPARQL, and SHACL constraints. We used a Mirai botnet and ontology quality metrics to demonstrate its effectiveness. The obtained results confirm the ability of IoTSecOnto to support knowledge sharing, automated reasoning, and improved threat analysis across diverse IoT settings.
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