基于机器学习的光纤窃听检测方法

陈孝莲; 秦奕; 张杰; 李亚杰; 宋浩鲲; 张会彬

doi:10.11959/j.issn.1000-0801.2020299

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基于机器学习的光纤窃听检测方法

研究与开发 | 更新时间：2024-06-05

- 基于机器学习的光纤窃听检测方法
- Optical fiber eavesdropping detection method based on machine learning
- 电信科学 2020年36卷第11期页码：61-67
- 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：
  
  [ "陈孝莲（1977- ），女，国网江苏省电力有限公司无锡供电分公司高级工程师，主要研究方向为电力通信" ]
  [ "秦奕（1979- ），男，国网江苏省电力有限公司无锡供电分公司高级工程师，主要研究方向为电力通信运维" ]
  [ "张杰（1972- ），男，博士，北京邮电大学信息光子与光通信研究院院长、博士生导师，主要研究方向为安全光通信技术" ]
  [ "李亚杰（1990- ），男，博士，北京邮电大学信息光子与光通信研究院在站博士后，主要研究方向为安全光通信技术" ]
  [ "宋浩鲲（1996- ），女，北京邮电大学信息光子与光通信研究院博士生，主要研究方向为安全光通信技术" ]
  [ "张会彬（1980- ），男，博士，北京邮电大学信息光子与光通信研究院讲师，主要研究方向为安全光通信技术" ]
- 基金信息：
  
  江苏省电力有限公司科技项目Science and Technology Project of Jiangsu Electric Power Co.,Ltd.(J2019124)
- DOI：10.11959/j.issn.1000-0801.2020299
  中图分类号： TP393
- 网络出版日期：2020-11，
  
  纸质出版日期：2020-11-20
- 稿件说明：
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陈孝莲, 秦奕, 张杰, 等. 基于机器学习的光纤窃听检测方法[J]. 电信科学, 2020,36(11):61-67.

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.
陈孝莲, 秦奕, 张杰, 等. 基于机器学习的光纤窃听检测方法[J]. 电信科学, 2020,36(11):61-67. DOI： 10.11959/j.issn.1000-0801.2020299.

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.

关键词

Keywords

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