Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Apr 2, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Multi-parameter deep learning for robust fiber eavesdropping detection and temporal localization.

Yingbo Fan, Yuang Li, Yuyuan Liang

|April 1, 2026

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Multi-state dynamically switchable frequency combs in cavity optomechanical-thermal systems.

Optics express·2026

Same author

Flexible temporal cloak with retrievable events.

Optics express·2026

Same author

Weak RF signals detection based on all-optical multi-mode oscillation with high performance.

Optics express·2026

Same author

Performance analysis of photon-limited PPM-FSO communications: SER under afterpulsing effect in practical photon-counting receivers.

Optics express·2025

Same author

Performance analysis of photon-limited free-space optical communications with practical photon-counting receivers.

Optics express·2025

Same author

Nonlinear dynamics of cavity optomechanical-thermal systems.

Optics express·2024

Detecting optical fiber eavesdropping is challenging due to environmental noise. A new hybrid deep learning model accurately identifies these threats by analyzing polarization and error vector magnitude, achieving 97.2% accuracy.

Area of Science:

Cybersecurity
Optical Communications
Signal Processing

Background:

Optical fibers are crucial for global data transmission but vulnerable to eavesdropping.
Eavesdropping attacks can occur without service interruption, posing security risks.
Environmental disturbances complicate eavesdropping detection, especially for machine learning models.

Purpose of the Study:

To develop a robust framework for detecting and localizing optical fiber eavesdropping.
To overcome challenges posed by environmental noise in eavesdropping detection.
To improve the accuracy and reliability of machine learning-based eavesdropping classifiers.

Main Methods:

Proposed a sensing framework utilizing a hybrid gated recurrent unit-convolutional neural network (GRU-CNN).

Related Experiment Videos

Last Updated: Apr 2, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Jointly analyzed polarization parameters and error vector magnitude for detection.

Conducted experiments across seven practical scenarios to validate the method.

Main Results:

Achieved a high detection accuracy of 97.2% for optical fiber eavesdropping.
Demonstrated robust performance under complex physical environments and disturbances.
Successfully enabled temporal localization of eavesdropping events.

Conclusions:

The proposed GRU-CNN framework offers accurate and robust detection of optical fiber eavesdropping.
The method effectively mitigates the impact of environmental disturbances on detection accuracy.
Developed integrated platforms for real-time and offline monitoring to facilitate deployment.