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Quantitative Resilience Assessment of Critical Infrastructures using High-Performance Simulations

Netkachov, O. (2023). Quantitative Resilience Assessment of Critical Infrastructures using High-Performance Simulations. (Unpublished Doctoral thesis, City, University of London)


Accessing the resilience of large cyber-physical systems (LCPS) is essential for ensuring the continuity of operations and minimising the impact of disruptions caused by natural disasters, cyberattacks, and other stressful events. Recent empirical studies of LCPS have demonstrated the usefulness of modelling and simulation in assessing properties that emerge from component interactions, including resilience. However, the sheer complexity of CIs poses challenges for modellers:

1) Resilience assessment requires high-fidelity models that include a probabilistic model of the system and adverse events of interest, such as accidental failures or malicious activities, and a physics simulation model of LCPS processes, such as power/liquid/gas flows.

2) Assessing resilience with high statistical significance requires a systematic exploration of the space of possible adverse events and recovery from their effects. Exploring this space requires a significant amount of effort.

This work offers solutions intended to help modellers overcome these difficulties by using the recent advances in modelling LCPSs and high-performance computing:

i) It offers a new modelling methodology for building agent-based hybrid hierarchical stochastic models using a new domain-specific language. The new modelling approach allows easy integration of a) a variety of modelling formalisms used to model cyber-attacks on CI/LCPS; and b) a set of deterministic models, as needed by the chosen level of fidelity and specific for the modelled CI. However, the deterministic models are not the focus of this work. Such models are assumed to exist in software available from third-party vendors.

ii) It presents a set of tools to support this methodology: the visual modeller and an extensible Monte Carlo simulation engine designed to utilise high-performance and cloud computing capabilities. The engine and the editor utilise modern development practices and technologies to provide a state-of-the-art solution.

This thesis provides a survey of the relevant literature, summarises the progress with the modelling methodology, and presents the results published to date with case studies based on an extended Nordic32, a reference architecture of a power transmission network with the SCADA subsystem. The studies explore the effects caused by adversaries targeting IT infrastructure and demonstrate the application of a defence-in-depth approach to reduce the effects of these attacks.

Publication Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
School of Science & Technology > School of Science & Technology Doctoral Theses
Doctoral Theses
[thumbnail of Netkachov Thesis 2023 PDF-A_Redacted.pdf]
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