Interpretable High-Pass Filter Fingerprint Model for 1000BASE-T Ethernet Authentication in IIoT

Industrial Internet of Things (IIoT) increasingly relies on Gigabit Ethernet (1000BASE-T) as the physical back-bone for interconnecting industrial devices, while the rapid growth of IIoT nodes has intensified concerns about physical-layer identity spoofing and unauthorized access. Recently, device fingerprinting has emerged as a promising approach to achieving secure authentication at the physical layer. However, existing 1000BASE-T fingerprint extraction methods rely on randomly scrambled signals, leading to degraded authentication reliability. In addition, the absence of radio-frequency (RF) frontend modules—commonly defined in wireless systems—within 1000BASE-T transmitters prevents the direct application of conventional hardware-imperfection models. To overcome these challenges, this paper first introduces test mode (TM) signals as highly consistent and controllable reference inputs, and on this basis, proposes an interpretable high-pass filter (HPF) fingerprint model. The model characterizes the high-pass response of the transmission link using a single-pole system, establishes a monotonic relationship between filter parameters and waveform morphology, and extracts stable fingerprint features accordingly. Furthermore, a closed-loop physical-layer authentication framework is developed, integrating signal acquisition, preprocessing, feature extraction, and device identification. Experimental results demonstrate that the proposed method achieves 100% identification accuracy under standard sampling conditions and preserves perfect recognition over the entire tested sampling-rate range. Moreover, the method exhibits substantially enhanced noise robustness compared with baseline methods, and retains 95.59% accuracy after a 30-day interval in temporal stability evaluations.