in the modern Internet of Things (IoT) paradigm, the future vision of communication networks is based on the extensive interconnection of diverse computing devices with the aim of forming an integrated network infrastructure called the Internet of Things. Through the provision of a wide range of intelligent applications and tools with remote control capabilities, IoT holds significant potential to facilitate various aspects of human life. However, security in IoT networks, due to the intrinsic characteristics of this ecosystem—particularly the heterogeneity of nodes in terms of resources and capabilities—poses a considerable technical challenge [1]. Trust management, relying on mechanisms for computing and analyzing the mutual trust level between nodes, plays a pivotal role in enabling secure and reliable communications within these networks and allows network nodes to make informed, trust-based decisions.

The main objective of distributed trust management systems is to predict the future behavior of nodes based on their past behavioral records. In this paper, a novel event-driven distributed trust management approach for IoT networks is presented.

The proposed method consists of a four-component system that leverages hybrid trust evaluation, machine learning-based prediction, dynamic trust adjustment and updating, and secure dissemination of trust information. This approach calculates the trust level between IoT entities using a combination of direct experiences, neighborhood recommendations, and behavior prediction. Simulation results indicate that, compared to the DDTMS method, the proposed approach achieves faster convergence, requires fewer transactions to identify malicious nodes, and exhibits better resilience against on-off attacks and slander attacks.

Keywords: Internet of Things, Trust Management, Distributed Trust Management, Trust Evaluation.