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Hybrid ontology for safety, security, and dependability risk assessments and Security Threat Analysis (STA) method for industrial control systems

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  • Alanen, Jarmo
  • Linnosmaa, Joonas
  • Malm, Timo
  • Papakonstantinou, Nikolaos
  • Ahonen, Toni
  • Heikkilä, Eetu
  • Tiusanen, Risto

Abstract

This paper introduces a model-based methodology for hybrid reliability, availability, maintainability, safety, and security (RAMSS) risk assessment management, which extends our previous work of model-based, data-driven, support for engineering mission-critical systems. It represents a hybrid risk assessment ontology, which harmonises basic concepts between dependability, safety and security based on well-known industrial standards. Based on the proposed ontology, we create a cybersecurity risk analysis method, called Security Threat Analysis (STA), for industrial control systems and successfully demonstrate the method. For the demonstration, we introduce a data model for creating a tool-supported data repository for STA, then implement this repository with a commercial-off-the-shelf tool. We use the repository to carry out an exemplary STA of a nuclear fuel pool cooling control system, assessing a cybersecurity-related hazard. The demonstration suggests that the hybrid RAMSS risk assessment ontology and the related STA data model are ready to be tested in industrial use, offering a structured data repository to support assessment and traceability between the created artefacts.

Suggested Citation

  • Alanen, Jarmo & Linnosmaa, Joonas & Malm, Timo & Papakonstantinou, Nikolaos & Ahonen, Toni & Heikkilä, Eetu & Tiusanen, Risto, 2022. "Hybrid ontology for safety, security, and dependability risk assessments and Security Threat Analysis (STA) method for industrial control systems," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:reensy:v:220:y:2022:i:c:s0951832021007444
    DOI: 10.1016/j.ress.2021.108270
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    1. Xia, Liqiao & Liang, Yongshi & Leng, Jiewu & Zheng, Pai, 2023. "Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

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