Date of Award

18-6-2025

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

First Advisor

Dr. Alan McGibney

Second Advisor

Dr. Susan Rea

Abstract

The rise of collaborative digital ecosystems and the growing adoption of Digital Twins (DTs) in sectors like smart manufacturing, energy, and autonomous sys- tems underscore the need for secure, interoperable frameworks. This research proposes a Secure Composite Digital Twin Architecture (CDT) that enables seam- less integration and collaboration among DTs, addressing key challenges in trust, interoperability, and governance. The architecture features a trust analyser to assess DT behaviour and a repu- tation model to enhance security as the ecosystem scales. It supports centralised, decentralised, and hybrid governance via distributed ledger technology, enabling dynamic policies and secure, token-based asset management. For cross-domain interoperability, it integrates advanced frameworks such as DTC, GAIA-X, and DigiTwins. To validate the architecture, a DT simulator was developed to replicate normal, unpredictable, and malicious behaviours. The trust analyser achieved 87% accuracy in detecting behaviour types. Accuracy slightly declined as the ecosystem scaled due to more marginal behaviour cases but remained above 85%. Reputation attack detection was highly precise for self-promotion and bad-mouthing, though false positives were higher for group attacks. Introducing more evaluation levels and leveraging architectural features like malicious DT elimination and governance protocols can further reduce false positives over time. Quantitative and qualitative evaluations against reference architectures (e.g., RAMI 4.0, ISO) highlight the proposed framework’s robustness, security, and compatibility with industry standards. Socially, this work aligns with UN Sustain- able Development Goals, promoting resource efficiency and security for critical infrastructures. Future directions include integrating AI and machine learning into the trust analyser and attack detection systems, expanding the architecture’s adaptability and intelligence in safeguarding collaborative digital ecosystems.

Access Level

info:eu-repo/semantics/openAccess

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