Machine-learning-based method for fiber-bending eavesdropping detection.
Opt Lett
; 48(12): 3183-3186, 2023 Jun 15.
Article
in En
| MEDLINE
| ID: mdl-37319057
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 60â
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Neural Networks, Computer
/
Machine Learning
Type of study:
Diagnostic_studies
Language:
En
Journal:
Opt Lett
Year:
2023
Type:
Article