Sub-Nyquist-Sampling-Based Device Fingerprint Extraction for Gigabit Ethernet

The proliferation of wired Gigabit Ethernet has greatly increased communication bandwidth, while introducing new challenges for device identification and security. Conventional physical-layer fingerprinting techniques are constrained by the Nyquist sampling theorem, limiting their suitability for large-scale deployment. To overcome this limitation, we propose a lightweight fingerprint extraction scheme based on sub-Nyquist sampling. The scheme introduces two types of fingerprints: the signal rearrangement distribution fingerprint (fsort) and the amplitude–frequency distribution fingerprint (fhist). The fsort adopts a low-complexity unsupervised classification framework based on amplitude rearrangement, principal component analysis (PCA), and a support vector machine (SVM), making it suitable for resource-constrained scenarios. The fhist establishes a high-accuracy supervised classification framework using amplitude–frequency statistical representations, linear discriminant analysis (LDA), and a deep learning classifier. Multi-instance and cross-type scenarios are used to evaluate classification accuracy and generalization capability. Experimental results show that the fhist method, employing the LDA-DL framework, achieves an accuracy of 97.4% in identifying 18 different devices at a sampling rate of 5 Msps. This approach reduces dependence on the sampling rate and data volume while maintaining high identification accuracy. It therefore provides a robust and cost-effective physical-layer authentication solution for Gigabit Ethernet.