Optical fiber eavesdropping detection method based on machine learning

Xiaolian CHEN; Yi QIN; Jie ZHANG; Yajie LI; Haokun SONG; Huibin ZHANG

doi:10.11959/j.issn.1000-0801.2020299

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Optical fiber eavesdropping detection method based on machine learning

Research and Development | 更新时间：2024-06-05

- Optical fiber eavesdropping detection method based on machine learning
- Telecommunications Science Vol. 36, Issue 11, Pages: 61-67(2020)
- 作者机构：
  
  1. 国网江苏省电力有限公司无锡供电分公司，江苏无锡 214000
  2. 北京邮电大学信息光子与光通信研究院，北京 100876
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020299
  CLC： TP393
- Published Online：2020-11，
  
  Published：20 November 2020
- 稿件说明：
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Xiaolian CHEN, Yi QIN, Jie ZHANG, et al. Optical fiber eavesdropping detection method based on machine learning[J]. Telecommunications science, 2020, 36(11): 61-67.
DOI：

Xiaolian CHEN, Yi QIN, Jie ZHANG, et al. Optical fiber eavesdropping detection method based on machine learning[J]. Telecommunications science, 2020, 36(11): 61-67. DOI： 10.11959/j.issn.1000-0801.2020299.

摘要

光纤窃听是信息安全的重大隐患之一，但其隐蔽性较高的特点导致筛查困难。针对通信网络中面临的光纤窃听问题，提出了基于机器学习的光纤窃听检测方法。首先基于窃听对传输物理层的影响，设计了 7 个维度的特征向量提取方法；其次通过实验，模拟窃听并收集特征向量，利用两种机器学习算法进行分类检测和模型优化。实验证明，神经网络分类算法的性能优于K近邻分类算法，其在10%分光窃听中可以实现98.1%的窃听识别率。

Abstract

Optical fiber eavesdropping is one of the major hidden dangers of power grid information security

but detection is difficult due to its high concealment.Aiming at the eavesdropping problems faced by communication networks

an optical fiber eavesdropping detection method based on machine learning was proposed.Firstly

seven-dimensions feature vector extraction method was designed based on the influence of eavesdropping on the physical layer of transmission.Then eavesdropping was simulated and experimental feature vectors were collected.Finally

two machine learning algorithms were used for classification detection and model optimization.Experiments show that the performance of the neural network classification is better than the K-nearest neighbor classification

and it can achieve 98.1% eavesdropping recognition rate in 10% splitting ratio eavesdropping.

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