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A simulation framework for automotive cybersecurity risk assessment
Simulation Modelling Practice and Theory, Volume: 136, Start page: 103005
Swansea University Authors: Siraj Shaikh , Hoang Nguyen
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© 2024 The Author(s). This is an open access article under the CC BY license.
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DOI (Published version): 10.1016/j.simpat.2024.103005
Abstract
Human-initiated disruptions such as cyberattacks on connected vehicles have the potential to cause cascading failures in transport systems, leading to systemic risks. ‘ISO/SAE 21434:2021 Road vehicles - Cybersecurity engineering’ is the current standard for risk management of road vehicles. However,...
Published in: | Simulation Modelling Practice and Theory |
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ISSN: | 1569-190X |
Published: |
Elsevier BV
2024
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Online Access: |
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URI: | https://cronfa.swan.ac.uk/Record/cronfa67382 |
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Abstract: |
Human-initiated disruptions such as cyberattacks on connected vehicles have the potential to cause cascading failures in transport systems, leading to systemic risks. ‘ISO/SAE 21434:2021 Road vehicles - Cybersecurity engineering’ is the current standard for risk management of road vehicles. However, the threat analysis and risk assessment framework given in the standard focuses on asset-level analysis and assessment. Hence, this study develops a novel simulation-based framework to perform threat analysis and risk assessment on connected vehicles from a transport network perspective. The proposed framework is developed based on the ISO/SAE 21434 threat analysis and risk assessment methodology. We demonstrate the applicability and usefulness of the framework through a remote attack via the cellular network on the in-vehicle communication bus system of a connected vehicle to show the potential impacts on the transport network. Based on the findings of our case studies, we exemplify how cyberattacks on individual system components of a connected vehicle have the potential to cause systemic failures. |
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Keywords: |
Connected vehicles; Automotive cybersecurity; Risk assessment; Simulation |
College: |
Faculty of Science and Engineering |
Funders: |
This work was supported by Coventry University and the A*STAR Research Attachment Programme (ARAP) . |
Start Page: |
103005 |