StackTrust: Intent-Based IoT Trust Management Framework for Secure Communications
Awan, Kamran Ahmad Din, Ikram Ud Almogren, Ahmad Guizani, Mohsen
Awan, Kamran Ahmad
Din, Ikram Ud
Almogren, Ahmad
Guizani, Mohsen
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Department
Machine Learning
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Journal article
Date
2025
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Language
English
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Abstract
The widespread adoption of Internet of Things (IoT) devices increases the need for trust management systems that adapt to dynamic conditions and maintain reliability under diverse threats. This paper introduces StackTrust, a trust management framework designed for scalable and precise IoT security. The framework integrates decision trees, support vector machines, and random forests within a logistic regression meta-learner to enhance classification robustness. A central feature is the adaptive weighting mechanism, which periodically adjusts the influence of each base model according to current performance metrics. To further stabilize predictions, a logarithmic historical-trust function incorporates long-term behavioral evidence while reducing sensitivity to short-term fluctuations. The combined trust score converges to a stable equilibrium under bounded model outputs. StackTrust supports both centralized and decentralized architectures and is validated through NS-3 simulations across multiple datasets and attack scenarios. Results on 45,000 instances confirm precision, recall, and F1-scores of 0.99, with computational complexity of O(N ×T) and O(M×T) to ensure efficiency for resource-constrained IoT environments.
Citation
K. A. Awan, I. U. Din, A. Almogren and M. Guizani, "StackTrust: Intent-Based IoT Trust Management Framework for Secure Communications," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3614654.
Source
IEEE Internet of Things Journal
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Keywords
Internet of Things, Trust Management, Ensemble Learning, Secure Communications, Privacy, Decentralized Systems, Heterogeneous Network, Malicious Nodes
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Publisher
IEEE