data about 10 optical module

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 96.88%. 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.

本文提出一种利用设备指纹增强光通信系统物理层安全的新方法，尤其针对无源光网络（Passive Optical Networks, PONs）。在该方案中，我们采用光开关键控（Optical On-Off Keying, OOK）调制进行信号传输，随后从这些OOK信号的眼图中提取独特的指纹特征。这些指纹特征随后通过孪生神经网络（Siamese neural networks）进行降维处理。随后，利用一组分类器对降维后的特征数据进行区分，从而在20公里单模光纤（Single-Mode Fiber, SSMF）传输中实现对多达10个光网络单元（Optical Network Units, ONUs）的稳健认证。值得注意的是，实验中获得的识别准确率达到96.88%。此外，当新设备接入网络时，该系统具备指纹特征的迁移学习能力，这一特性加快了新上线设备的认证速度。