Identify the Device Fingerprint of OFDM-PONs With a Noise-Model-Assisted CNN for Enhancing Security

Abstract

Device fingerprint can be utilized in optical communication system to strengthen the physical layer security for its uniqueness and unforgeability. In this letter, we propose and demonstrate a noise-model-assisted feature extraction method to reveal the device fingerprint hidden in the transmitted signal. Our scheme is verified in orthogonal frequency division multiplexing-passive optical network (OFDM-PON). First, the additive and multiplicative noise in normal data signal is extracted and two-dimensional feature matrix is formed. Then, a trained convolutional neural network (CNN) is used as a classifier to identify the fingerprint from the feature matrix. Experimental results show that our method achieves a high identification accuracy up to 99.25%. In the meanwhile, the loss function and training accuracy have an excellent performance. The ability of identifying rogue optical network unit (ONU) is also tested and the identification accuracy is 100%. With the noise-model-assisted CNN, the physical layer security of the system is adequately enhanced under the comprehensive consideration of the ability of identifying legal ONU and resisting illegal ONU.

Keywords

• Physical layer security
• device fingerprint
• deep learning
• orthogonal frequency division multiplexing passive optical network (OFDM-PON)