A Design of Deep Learning Based Optical Fiber Ethernet Device Fingerprint Identification System

This paper proposes a novel deep learning based hardware fingerprint identification method for optical fiber Ethernet devices. An adjacent constellation trance figure (ACTF) feature extraction method is firstly introduced for baseband modulation system with only amplitude waveform. A 2-dimensional convolutional neural network (2D-CNN) is designed to classify different optical fiber Ethernet devices via ACTF features. An intensity modulation / direct detection (IM/DD) experimental system with 24 optical fiber Ethernet devices is designed for evaluations. We optimize the ACTF parameter setups and compare the classification accuracy with another deep learning based long short-term memory (LSTM) network and classical statistical feature methods. Experimental results show that our proposed ACTF-CNN can achieve a classification accuracy as high as 99.49% and 96.29% under SNR levels of 30 dB and 10 dB, respectively, which significantly outperforms LSTM network and statistical feature based methods.