Variational autoencoder-assisted unsupervised hardware fingerprint authentication in a fiber network.
Opt Lett
; 49(8): 2029-2032, 2024 Apr 15.
Article
en En
| MEDLINE
| ID: mdl-38621068
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.
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Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Opt Lett
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos