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A Physical-Layer Threat Detection Framework for Secure IoT and Smart Grid Networks Using HHT-Based Multimodal Deep Learning
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DOI：10.3390/technologies14070423.png)
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Abstract
En 中文
Secure IoT and smart grid networks depend on reliable hardware operation to maintain continuous service and system availability. Physical-layer abnormalities such as partial discharge (PD) can weaken infrastructure components and disrupt connected systems before conventional monitoring methods detect the problem. PD is one of the earliest indicators of abnormal hardware activity in electrical infrastructure. If it is not detected in time, it can damage equipment, reduce system reliability, and increase the risk of service interruption in intelligent network environments. Existing detection methods often struggle with PD signals because these signals are non-stationary, vary over time, and frequently contain noise. This limits reliable physical-layer threat detection in secure IoT and smart grid networks. This study presents an integrated physical-layer threat-detection framework for secure IoT and smart grid networks that combines adaptive HHT-based signal decomposition with multimodal deep learning for early hardware threat identification. The framework first applies the Hilbert–Huang Transform (HHT) to decompose PD signals and extract time–frequency features that describe discharge behavior. A convolutional neural network with an attention-based fusion mechanism then learns patterns from electrical and acoustic signals. The model classifies hardware condition into normal operation, early abnormal activity, and critical discharge states associated with potential hardware threats. The framework is evaluated using two public datasets: the Dataset of Partial Discharge and Noise Signals and the Partial Discharge Localization (PD-Loc) dataset available through the IEEE DataPort. Experimental evaluation shows that the proposed framework achieves 97.8% detection accuracy, a 97.0% F1-score, and an average AUC of 0.98. The framework maintains 94.6% accuracy under severe noise conditions (10 dB SNR) and performs inference in approximately 12 ms per sample. Furthermore, component-wise analysis further shows that HHT-based feature extraction improves detection accuracy from 91.8% to 95.6%, while multimodal learning increases the final accuracy to 97.8%.
Keywords:
physical-layer threat detection
intelligent networks
secure IoT
multimodal deep learning
Hilbert–Huang transform
partial discharge detection
anomaly detection
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