Telecommunications Science ›› 2020, Vol. 36 ›› Issue (11): 61-67.doi: 10.11959/j.issn.1000-0801.2020299

• Research and Development • Previous Articles     Next Articles

Optical fiber eavesdropping detection method based on machine learning

Xiaolian CHEN1,Yi QIN1,Jie ZHANG2,Yajie LI2,Haokun SONG2,Huibin ZHANG2   

  1. 1 Wuxi Power Supply Company,State Grid JiangSu Electric Power Co.,Ltd.,Wuxi 214000,China
    2 State Key Laboratory of Information Photonics and Optical Communications,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Revised:2020-09-07 Online:2020-11-20 Published:2020-12-09
  • Supported by:
    Science and Technology Project of Jiangsu Electric Power Co.,Ltd.(J2019124)


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.

Key words: eavesdropping detection, fiber eavesdropping, machine learning, neural network

CLC Number: 

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