Original scientific paper
https://doi.org/10.32985/ijeces.17.7.2
A Hybrid CWT–Transformer Method for Traffic Fingerprinting of Smart-Grid Device
Yunhao Yv
; Guizhou Power Grid Co Ltd Guiyang, Guizhou, China
Fuhua Luo
; Guizhou Power Grid Co Ltd Guiyang, Guizhou, China
Gaoxin Lyu
; University of Science and Technology Beijing
*
Fu Yizhou
; Guizhou Power Grid Co Ltd Guiyang, Guizhou, China
Yao Li
; Guizhou Power Grid Co Ltd Guiyang, Guizhou, China
* Corresponding author.
Abstract
Device identification commonly relies on machine learning models using radio frequency (RF) signals, decrypted payload features or traditional statistical traffic features. However, in real-world power grid environments, complex electromagnetic interference makes the stable collection of RF signals difficult. Furthermore, non-intrusive traffic decryption is challenging to implement in engineering practice, and traditional statistical features struggle to accurately depict the highly non-stationary and bursty characteristics of power system traffic. The paper proposes a decryption-free device traffic fingerprinting method based on a Hybrid CWT-Transformer model. The model enables reliable device identification by capturing multi-scale transient patterns and long-range dependencies directly from encrypted traffic. First, the paper utilizes the Continuous Wavelet Transform (CWT) to convert session-level statistics traffic into time- frequency maps, which accurately represent the highly non-stationary and bursty nature of the device traffic. Subsequently, a Convolutional Stem (Conv Stem) extracts local patterns, and a deformable patch embedding focuses the representation on informative time–frequency regions. Finally, the paper uses a Transformer encoder to model global dependencies for device classification. Experimental results on a real-world Power IoT dataset (16 device classes) from China Southern Power Grid demonstrate that the proposed model achieves 98.50% accuracy, significantly outperforming mainstream methods in precision, recall, and F1-score.
Keywords
Smart grid; Device identification; Device traffic fingerprinting; CWT; Vision Transformer (ViT);
Hrčak ID:
348736
URI
Publication date:
1.7.2026.
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