Epiphaniou, G, Hammoudeh, M, Yuan, H, Maple, C and Ani, U (2023) Digital twins in cyber effects modelling of IoT/CPS points of low resilience. Simulation Modelling Practice and Theory, 125. ISSN 1569-190X

[thumbnail of 1-s2.0-S1569190X23000229-main.pdf]
Preview
Text
1-s2.0-S1569190X23000229-main.pdf - Published Version

Download (1MB) | Preview

Abstract

The exponential increase of data volume and velocity have necessitated a tighter linkage of physical and cyber components in modern Cyber–physical systems (CPS) to achieve faster response times and autonomous component reconfiguration. To attain this degree of efficiency, the integration of virtual and physical components reinforced by artificial intelligence also promises to improve the resilience of these systems against organised and often skillful adversaries. The ability to visualise, validate, and illustrate the benefits of this integration, while taking into account improvements in cyber modelling and simulation tools and procedures, is critical to that adoption. Using Cyber Modelling and Simulation (M&S) this study evaluates the scale and complexity required to achieve an acceptable level of cyber resilience testing in an IoT-enabled critical national infrastructure (CNI). This research focuses on the benefits and challenges of integrating cyber modelling and simulation (M&S) with digital twins and threat source characterisation methodologies towards a cost-effective security and resilience assessment. Using our dedicated DT environment, we show how adversaries can utilise cyber–physical systems as a point of entry to a broader network in a scenario where they are trying to attack a port.

Item Type: Article
Additional Information: © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 11 Apr 2023 15:31
Last Modified: 11 Apr 2023 15:31
URI: https://eprints.keele.ac.uk/id/eprint/12119

Actions (login required)

View Item
View Item