Correction to: Defending against product-oriented cyber-physical attacks on machining systems

Shafae, Mohammed; Wells, Lee J.; Purdy, Gregory T.

doi:10.1007/s00170-019-04576-3

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Protecting the Cyberphysical System Against Intrusion Dection

Sharma

2023

2023 2nd International Conference on Futuristic Technologies (INCOFT)

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Protecting the Cyberphysical System Against Intrusion Dection

Sharma

2023

2023 2nd International Conference on Futuristic Technologies (INCOFT)

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Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment

Rahman,

Hamedani,

Son

et al. 2024

Journal of Computing and Information Science in Engineering

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Identifying, analyzing, and evaluating cybersecurity risks is essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems is lacking. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors' techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.

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Correction to: Defending against product-oriented cyber-physical attacks on machining systems

Abstract: The original version of this article contained a mistake. While Table 4 is mentioned within the article text several times, the actual Table 4 was missing in the original version of the article. Please see Table 4 below, The original article has been corrected.

Cited by 2 publications

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Protecting the Cyberphysical System Against Intrusion Dection

Protecting the Cyberphysical System Against Intrusion Dection

Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment

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