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StackTrust: Intent-Based IoT Trust Management Framework for Secure Communications
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DOI：10.1109/JIOT.2025.3614654.png)
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Abstract
En 中文
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 article introduces StackTrust, a trust management framework designed for scalable and precise IoT security. The framework integrates decision trees, support vector machines (SVMs), and random forests within a logistic regression metalearner 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula>-scores of 0.99, with computational complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N \times T)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(M \times T)$ </tex-math></inline-formula> to ensure efficiency for resource-constrained IoT environments.
Keywords:
Decentralized systems
ensemble learning
heterogeneous network
Internet of Things (IoT)
malicious nodes
privacy
secure communications
trust management
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W
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