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Abstract With the rapid growth of resource integration in modern power systems, these resources are diverse, large-scale, and situated in complex and open physical environments, making them relatively vulnerable to cyber-attacks due to weaker security measures. To address this challenge, this paper proposes an identity authentication architecture system that integrates software and hardware. In the software fingerprint section, we extract packet characteristics and statistical features through network probing, and combine them with time difference sequence features obtained from side-channel monitoring to generate the software fingerprint of the power smart terminal by direct concatenation. This method incorporates various characteristic information, enhancing the recognition accuracy of the fingerprint features. In the hardware fingerprint section, we generate hardware fingerprints by extracting the preamble signal and performing statistical feature analysis. Finally, using an ensemble learning method, we integrate the software and hardware fingerprints to generate device fingerprint features. This approach effectively addresses the security authentication issue of power equipment based on High-Level Power Line Communication (HPLC), achieving a recognition rate of over 95% under most machine learning classification methods.
Chen et al. (Thu,) studied this question.