Two-Way Intelligent Trust Management Using Machine Learning and Subjective Logic in FOG

The rapid proliferation of the Internet of Things (IoT) has intensified concerns regarding trust, security, and reliability in large-scale, dynamic, and resource-constrained environments. Conventional trust management approaches—primarily based on reputation, rule-based reasoning, or static evidence aggregation—struggle to adapt to evolving behaviors and sophisticated attacks such as collusion and Sybil attacks. Moreover, most existing solutions adopt a one-way trust model, neglecting the inherently mutual nature of trust between IoT devices and fog infrastructures. This paper proposes TWITMF, a Two-Way Intelligent Trust Management Framework that integrates machine learning (ML) with Subjective Logic (SL) to enable adaptive, uncertainty-aware, and bidirectional trust evaluation in IoT–fog environments. In the proposed framework, lightweight ML models learn behavioral patterns and generate predictive trust evidence, while Subjective Logic explicitly models uncertainty and fuses both direct and indirect evidence into interpretable trust opinions. Unlike ML-only approaches that produce point estimates, TWITMF treats ML outputs as evidence rather than final decisions, allowing robust trust reasoning under sparse or conflicting observations. The framework supports mutual trust assessment, enabling both IoT devices and fog nodes to evaluate each other prior to interaction. Extensive simulation-based experiments conducted in a fog-enabled IoT environment demonstrate that TWITMF significantly outperforms reputation-based, ML-only, and SL-only baselines. The proposed framework achieves up to 95% F1-score, reduces detection latency, and exhibits strong resilience against coordinated collusion and Sybil attacks, while maintaining low computational overhead suitable for real-time deployment. These results confirm the effectiveness of combining data-driven learning with uncertainty-aware reasoning for secure and reliable trust management in next-generation IoT applications such as smart cities, healthcare monitoring, and intelligent transportation systems