Trust and Security Analyzer for Digital Twins

Connecting multiple digital twins aids in creating collaborative ecosystems that provide all participants with a holistic view of systems, products or components. Trust and security are always crucial in a collaborative environment. This work proposes a trust and security analyzer to evaluate individual digital twins in the ecosystem and classify their behaviour based on their actions. This classification will help ecosystem participants to make decisions about other digital twins, such as if it meets specific security criteria or interaction can lead to additional risk for the overall system operation. To validate the proposed analyzer accuracy, this paper implemented a digital twin simulator that can be used to emulate digital twin interactions. The proposed analyzer uses statistical analysis for the digital twin type classification, showing more than 94% accuracy for detecting malicious digital twins while maintaining low false-positive rates.