We propose and demonstrate an identity authentication method in the orthogonal frequency division multiplexing passive optical network (OFDM-PON) by recognizing device fingerprints of optical network units (ONUs). Signal decomposition methods based on wavelet transform are implemented to extract feature matrixes during the pre-process of samples, and then a trained 2-D convolutional neural network (2D-CNN) is applied to classify and identify these feature matrixes. Experimental results show that the identity of legitimate ONUs can be successfully recognized and 97.41% identification accuracy is achieved. A rogue ONU can be detected with an identification accuracy of 100%, which indicates that the ability of PONs to resist identity spoofing attack is effectively improved. The robustness of the scheme is also verified. With the proposed strategy, the security level of PON system at the physical layer can be increased markedly.

中文翻译：

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使用硬件指纹认证增强 OFDM-PON 的物理层安全性：一种机器学习方法

我们通过识别光网络单元 (ONU) 的设备指纹，在正交频分复用无源光网络 (OFDM-PON) 中提出并演示了一种身份认证方法。在样本的预处理过程中实现基于小波变换的信号分解方法来提取特征矩阵，然后应用训练的二维卷积神经网络（2D-CNN）对这些特征矩阵进行分类和识别。实验结果表明，可以成功识别合法ONU的身份，识别准确率达到97.41%。流氓ONU能够以100%的识别准确率被检测出来，这表明PON抵抗身份欺骗攻击的能力得到有效提升。方案的稳健性也得到验证。根据提议的策略，