A Real-Time Trust Management Model Using Digital Twin in IoT Networks

Abstract

The Internet of Things (IoT) is becoming more and more entwined with both our private lives and business environments. The IoT's expanding relevance motivates researchers to develop models that examine IoT device activity as a means of determining trustworthiness and detecting unusual behavior. This paper aims to develop a new trust model based on digital twins to detect and foretell anomalies in real IoT setups. To build trust, twins communicate constantly and warn one another when their physical counterparts communicate. The notified twins then examine specific factors of the communicating nodes, such as traceability, residual energy, resource usage, etc., to detect anomalies and take appropriate actions. We evaluate the performance and applicability of our model using the iFogSim simulator, mainly considering the probability of detecting anomalies. The simulation demonstrates improvement in trust management, scalability, and resource efficiency achieving optimizing performance in terms of energy consumption, execution time, and network usage. We demonstrate around 95% accuracy rate in identifying compromised nodes, including during DoS attacks.