An Adaptive and Resilient Trust Management Model for Social IoT: A Reinforcement Approach

ABSTRACT The Social Internet of Things (SIoT) allows smart devices to form social relationships for efficient service discovery and resource sharing. However, trust management is challenged by attacks like ballot stuffing and bad mouthing, which manipulate trust scores through biased recommendations. Existing approaches often evaluate recommenders based on their service provider role, overlooking their behavior as recommenders due to data sparsity. This paper presents a resilient trust management model using reinforcement learning. It combines multiple trust features—direct trust, service reliability, social ties, recommendation benevolence, and referred trust—to compute trust‐based rewards. A filtering mechanism is also introduced to detect dishonest recommenders and reduce the impact of biased feedback. Experimental results demonstrate strong resilience against Bad mouthing attack (BMA), ballot stuffing attack (BSA), and Sybil attacks compared to state‐of‐the‐art models, along with faster convergence on both SIoT/IoT network and Epinions datasets. These findings confirm the model's effectiveness in preserving trust and resisting attacks in SIoT environments.