WPD-ResNeSt:Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer Learning  

作　　者：Ting Yang Yucheng Hou Yachuang Liu Feng Zhai Rongze Niu 

机构地区：[1]Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin 300072,China [2]China Electric Power Research Institute,Beijing 100192,China [3]Henan Electric Power Research Institute,Zhengzhou 450052,China

出　　处：《CSEE Journal of Power and Energy Systems》2024年第6期2610-2620,共11页中国电机工程学会电力与能源系统学报（英文）

基　　金：supported in part by the Science and Technology Project of State Grid Corporation of China(SGHADK00PJJS2000026).

摘　　要：With the advancement of new infrastructures,the digitalization of the substation communication network has rapidly increased,and its information security risks have become increasingly prominent.Accurate and reliable substation communication network flow models and flow anomaly detection methods have become an important means to prevent network security problems and identify network anomalies.The existing substation network analyzers and flow anomaly detection algorithms are usually based on threshold determination,which cannot reflect the inherent characteristics of substation automation flow based on IEC 61850 and have low detection accuracy.To effectively detect abnormal traffic,this paper fully explores the substation network traffic rules,extracts the frequency domain features of the station level network,and designs an abnormal traffic identification model based on the ResNeSt convolutional neural network.Transfer learning is used to solve the problem of insufficient abnormal traffic labeled samples in the substation.Finally,a new method of abnormal traffic detection in smart substation station level communication networks based on deep transfer learning is proposed.The T1-1 substation communication network is constructed on OPNET for abnormal simulations,and the actual network traffic in a 110kV substation is fused with CIC DDoS2019 and KDD99 data sets for the algorithm performance test,respectively.The accuracy reached is 98.73%and 98.95%,indicating that the detection model proposed in this paper has higher detection accuracy than existing algorithms.

关 键 词：Anomaly traffic detection deep learning substation station level communication network traffic model 

分 类 号：TM76[电气工程—电力系统及自动化] TP39[自动化与计算机技术—计算机应用技术]