TrustAware-GNN: Graph Neural Network-Based Trust Management for IoT Anomaly Detection
Awan, Kamran Ahmad Din, Ikram Ud Almogren, Ahmad Han, Zhu Guizani, Mohsen
Awan, Kamran Ahmad
Din, Ikram Ud
Almogren, Ahmad
Han, Zhu
Guizani, Mohsen
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
The widespread deployment of Internet of (IoT) devices has intensified the demand for scalable and secure trust management. Existing GNN-based approaches often neglect real-time adaptability and contextual trust in dynamic, heterogeneous networks. This study introduces TrustAware-GNN, a trust-aware graph neural network framework designed to robustly evaluate device trustworthiness in IoT environments. The model integrates a multi-dimensional trust mechanism encompassing direct, indirect, temporal, and contextual trust, computed using localized device parameters: reliability, capability, security posture, reputation, and location awareness. Trust values modulate edge weights within the graph, enabling trust-adaptive message propagation. A trust-threshold-based edge formation mechanism filters unreliable links, while attention-based aggregation refines node embeddings. The model continuously adapts to behavioral shifts via time-decayed trust updates and contextual similarity matching. Simulation was conducted across IoT-23, EDGE-IIoTSET, AutoTrust, and ToN-IoT datasets. TrustAware-GNN achieved 94.83% accuracy on EDGE-IIoTSET and 93.25% on IoT-23, outperforming MGNN, STAR-GCN, and SEGC-PP in both accuracy and adaptability under dynamic trust scenarios.
Citation
K. A. Awan, I. U. Din, A. Almogren, Z. Han and M. Guizani, "TrustAware-GNN: Graph Neural Network-Based Trust Management for IoT Anomaly Detection," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3584653.
Source
IEEE Internet of Things Journal
Conference
Keywords
Anomaly Detection, Graph Neural Network, Internet of Things, Security & Privacy, Trust Management
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Publisher
IEEE