Enhancing the security of OFDM-PONs with machine learning based device fingerprint identification

We propose and demonstrate an identity authentication method in OFDM-PON by recognizing device fingerprints of ONUs. Experimental results show that rogue ONU can be detected and 92.43% identification accuracy is achieved. The proposed method can effectively improve the ability of PONs to resist identity spoofing attack.