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- W4367607846 abstract "In this Letter, we present a scheme for detecting fiber-bending eavesdropping based on feature extraction and machine learning (ML). First, 5-dimensional features from the time-domain signal are extracted from the optical signal, and then a long short-term memory (LSTM) network is applied for eavesdropping and normal event classification. Experimental data are collected from a 60 km single-mode fiber transmission link with eavesdropping implemented by a clip-on coupler. Results show that the proposed scheme achieves a 95.83% detection accuracy. Furthermore, since the scheme focuses on the time-domain waveform of the received optical signal, additional devices and a special link design are not required." @default.
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- W4367607846 date "2023-06-07" @default.
- W4367607846 modified "2023-10-17" @default.
- W4367607846 title "Machine-learning-based method for fiber-bending eavesdropping detection" @default.
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- W4367607846 doi "https://doi.org/10.1364/ol.487214" @default.
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