Year: 2026
Type: article
Abstract: 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.
Source: Symmetry
DOI: 10.3390/sym18020339
Authors: Youdong Wang, Yu Jiang
Institutions: Southeast University, Purple Mountain Laboratories
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G6958290864, G8014458112
OpenAlex ID: W7128678378