Enhancing the Physical Layer Security of OFDM-PONs With Hardware Fingerprint Authentication: A Machine Learning Approach

Abstract

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.