Digital Twins (DTs) are increasingly deployed in collaborative ecosystems, enabling adaptive monitoring, simulation, and decision-making across interconnected systems. Similar to Internet of Things (IoT) systems, DTs rely on real-time data collection, analysis, and feedback loops to represent and manage physical entities. Both technologies generate large volumes of data and leverage AI/ML techniques to optimise operations, making them complementary in building intelligent, adaptive ecosystems. However, the heterogeneous and dynamic nature of these ecosystems introduces challenges in assessing trustworthiness and ensuring reliable collaboration. This paper presents a Trust Analyser for behavioural categorisation of DTs, leveraging key Trust Evaluation Categories including safety, privacy, security, reliability, resilience, uncertainty & dependability, and ecosystem goal alignment. A DT simulator is developed to generate normal, unpredictable, and malicious DT behaviours, facilitating controlled experiments in a scalable DT ecosystem. Experimental results demonstrate that the TA achieves high accuracy, above 85% in detecting DT behavioural types, maintaining robust performance even as the ecosystem scales, with minor reductions attributable to network-induced delays and overlapping behaviour patterns. The proposed approach highlights the effectiveness of trust-based behavioural analysis for ensuring resilient, secure, and accountable operation in complex collaborative ecosystems.
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Trust-Based Digital Twin Behavioural Categorisation in a Collaborative Ecosystem
Semantic Scholar · Computer Science · 2025
Abstract
Digital Twins (DTs) are increasingly deployed in collaborative ecosystems, enabling adaptive monitoring, simulation, and decision-making across interconnected systems. Similar to Internet of Things (IoT) systems, DTs rely on real-time data collection, analysis, and feedback loops to represent and manage physical entities. Both technologies generate large volumes of data and leverage AI/ML techniques to optimise operations, making them complementary in building intelligent, adaptive ecosystems. However, the heterogeneous and dynamic nature of these ecosystems introduces challenges in assessing trustworthiness and ensuring reliable collaboration. This paper presents a Trust Analyser for behavioural categorisation of DTs, leveraging key Trust Evaluation Categories including safety, privacy, security, reliability, resilience, uncertainty & dependability, and ecosystem goal alignment. A DT simulator is developed to generate normal, unpredictable, and malicious DT behaviours, facilitating controlled experiments in a scalable DT ecosystem. Experimental results demonstrate that the TA achieves high accuracy, above 85% in detecting DT behavioural types, maintaining robust performance even as the ecosystem scales, with minor reductions attributable to network-induced delays and overlapping behaviour patterns. The proposed approach highlights the effectiveness of trust-based behavioural analysis for ensuring resilient, secure, and accountable operation in complex collaborative ecosystems.