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An Ontology-Driven Knowledge-Based System for Modeling Cybersecurity Architectures in Smart Cities and Open Data Environments

Vladimír Soběslav et al.

Journal of Cases on Information Technology2026https://doi.org/10.4018/jcit.399500article
ABDC C
Weight
0.50

What the paper says

Ensuring cybersecurity in smart cities (SCs) presents a complex, multidimensional challenge that requires robust methodologies for threat identification, risk assessment, and the development of effective countermeasures. This article presents a web-based expert system powered by ontologies, designed to assist cybersecurity architects in modeling and analyzing security scenarios in SC environments, with a particular emphasis on open data. The system employs formal ontologies to structure cybersecurity knowledge, including assets, threats, vulnerabilities, and countermeasures, and uses automated reasoning to support informed decision-making and risk mitigation. The case study is based on Business Process Model and Notation extended for Smart Cities (BPMN-SC), tailored to SC services and data flows. The results demonstrate that an ontology-driven expert system can enhance transparency, consistency, and formal rigor in cybersecurity governance within SC initiatives driven by open data.

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https://doi.org/https://doi.org/10.4018/jcit.399500

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@article{vladimír2026,
  title        = {{An Ontology-Driven Knowledge-Based System for Modeling Cybersecurity Architectures in Smart Cities and Open Data Environments}},
  author       = {Vladimír Soběslav et al.},
  journal      = {Journal of Cases on Information Technology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/jcit.399500},
}

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An Ontology-Driven Knowledge-Based System for Modeling Cybersecurity Architectures in Smart Cities and Open Data Environments

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.