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Details of Grant 

EPSRC Reference: EP/V039156/1
Title: Security of Digital Twins in Manufacturing
Principal Investigator: Clark, Professor JA
Other Investigators:
Hierons, Professor R Popescu, Dr A A Law, Dr J A
Gope, Dr P
Researcher Co-Investigators:
Project Partners:
Department: Computer Science
Organisation: University of Sheffield
Scheme: Standard Research
Starts: 07 October 2021 Ends: 06 April 2024 Value (£): 619,963
EPSRC Research Topic Classifications:
Artificial Intelligence Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:
Manufacturing Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
10 Feb 2021 Cross-RI PaCCS 2020 prioritisation panel Announced
Summary on Grant Application Form
Increasing productivity in manufacturing is a critical economic goal of the UK government and digitisation has been proposed as the cornerstone of achieving that. The MadeSmarter review makes a case for widespread digitisation across sectors (including manufacturing) and indicates the economic benefits that would accrue to the UK in doing so. It also draws significant attention to the role to be played by so-called Digital Twins.

Gartner has defined a digital twin as "a software design pattern that represents a physical object with the objective of understanding the asset's state, responding to changes, improving business operations and adding value" and describing a DT as "a digital representation of a real-world entity or system." The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction." For advanced manufacturing a DT has been described by the AMRC as "a live digital coupling of the state of a physical asset or process to a virtual representation with a functional output." Functional output here means information sent to a system or human observer that is actionable to deliver value.

There are many views on the precise nature of Twins Twins. Loosely speaking, there is a physical system or sensors, actuators and other assets or entities of which a "digital mirror" is maintained. Essentially, this is some digital model of important aspects of the system. The AMRC definition draws attention to the real-time ("live") nature of Digital Twins in manufacturing. This digital model can serve many purposes, from acting as the vehicle for remote interaction with the system by its operators (and remote operation has acquired a new importance in the light of the need to develop resilience to pandemics) to being the prmiary reference model over which intrusions are detected. Digital Twins have been identified by Gartner as one of the major technologies of our time.

Since Digital Twins are perceived as fundamental to value generation by systems so it is no surprise that their security has arisen as a problem. They may encapsulate important IPR and provide the most up to reference for the system's state. That information itself may be confidential and its integrity is critical to the effectiveness of a system to deliver ts business goals.

Understanding of the security of Digital Twins is limited. There has hardly been any reseach in this area. In this proposal we advance a wide-ranging initial programme of work that will engage stakeholders and lead eventually to a comprehensive understanding of security priorities concerning Digital Twins. Our programme mixes concrete research with engagement and roadmapping. It fuses the use of formal mathematic approaches to specification of systems and proofs of their properties, through to exploiting machine learning to detect intrusions. Our proposal also brings to bear expertise in manufacturing, robtics and control engineering. It is significantly interdisciplinary.

At its conclusion we will have a community aware of the risks of Digital Twins and with a fully informed sense of priorities for research and innovation. We will initiate new areas of research but also seek to understand the potential for cross-pollination and transfer of research insights from other domains.

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Organisation Website: http://www.shef.ac.uk