ABSTRACT
Physical-layer authentication (PLA) based on hardware fingerprints can safeguard optical networks against large-scale masquerade or active injection attacks. However, traditional schemes rely on massive labeled close-set data. Here, we propose an unsupervised hardware fingerprint authentication based on a variational autoencoder (VAE). Specifically, the triplets are generated through variational inference on unlabeled optical spectra and then applied to train the feature extractor, which has an excellent generalization ability and enables fingerprint feature extraction from previously unknown optical transmitters. The feasibility of the proposed scheme is experimentally verified by the successful classification of eight optical transmitters after a 20â km standard single-mode fiber (SSMF) transmission, to distinguish efficiently the rogue from legal devices. A recognition accuracy of 99% and a miss alarm rate of 0% are achieved even under the interference of multiple rogue devices. Moreover, the proposed scheme is verified to have a comparable performance with the results obtained from supervised learning.
ABSTRACT
Physical-layer secure key distribution (PLSKD) generally acquires highly correlated entropy sources via bidirectional transmission to share the channel reciprocity. For long-haul fiber links, the non-negligible backscattering noise (BSN) and the challenge of bidirectional optical amplification degrade the key generation performances. Since the channel reciprocity can be precisely mapped using neural networks (NNs), unidirectional PLSKD provides a feasible PLSKD for longer fiber links. Here, a final error-free key generation rate (KGR) in unidirectional PLSKD of 3.07â Gb/s is demonstrated over a 300â km fiber link using NNs. Moreover, the channel mapping is analyzed in terms of fiber distance, chromatic dispersion, the nonlinearity of random source, and BSN.