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。
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.
作者
陈孝莲
秦奕
张杰
李亚杰
宋浩鲲
张会彬
CHEN Xiaolian;QIN Yi;ZHANG Jie;LI Yajie;SONG Haokun;ZHANG Huibin(Wuxi Power Supply Company,State Grid JiangSu Electric Power Co.,Ltd.,Wuxi 214000,China;State Key Laboratory of Information Photonics and Optical Communications,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《电信科学》
2020年第11期61-67,共7页
Telecommunications Science
基金
江苏省电力有限公司科技项目(No.J2019124)。
关键词
窃听检测
光纤窃听
机器学习
神经网络
eavesdropping detection
fiber eavesdropping
machine learning
neural network