基于机器学习的光纤窃听检测方法

doi:10.11959/j.issn.1000-0801.2020299

Abstract

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.

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

CLC Number:

TP393

Xiaolian CHEN,Yi QIN,Jie ZHANG,Yajie LI,Haokun SONG,Huibin ZHANG. Optical fiber eavesdropping detection method based on machine learning[J]. Telecommunications Science, 2020, 36(11): 61-67.

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