학술논문
Hardware Fingerprint Authentication Based on Siamese Neural Networks in PON
Document Type
Periodical
Source
IEEE Photonics Technology Letters IEEE Photon. Technol. Lett. Photonics Technology Letters, IEEE. 36(7):508-511 Apr, 2024
Subject
Language
ISSN
1041-1135
1941-0174
1941-0174
Abstract
This letter presents a novel approach to bolster the physical layer security of optical communication systems, specifically within Passive Optical Networks (PONs), through the utilization of device fingerprints. In this proposed scheme, we employ Optical On-Off Keying (OOK) modulation for signal transmission and subsequently extract distinct fingerprint features from the eye diagrams of these OOK signals. These fingerprint features are then subjected to dimensionality reduction via Siamese neural networks. Subsequently, a set of classifiers is utilized to discriminate among the downscaled feature data, thereby achieving robust authentication for up to 10 ONUs in a 20 km Single-Mode Fiber (SSMF) transmission. Remarkably, the recognition accuracy attained in our experiments reached 96.04%. Moreover, this system exhibits the capacity for transfer learning of fingerprint features when new devices are introduced into the network. This feature speeds up the authentication of new devices coming online.