摘要
基于OPC技术建立电力监控系统,并结合蜜罐与蜜网技术构建系统网络安全风险预警平台,通过3D预警网格模型及3D卷积神经网络方法执行对监控系统的网络安全态势感知与风险预警任务。测试结果显示,文中设计的电力监控系统无论是仪器设备与OPC服务器之间,还是OPC客户端与OPC服务器之间,通信效率均可保证,另外,电力监控系统网络安全风险预警平台所用方法对入侵风险的检测与预警准确率、精度、召回率与假阳性均高于支持向量机与朴素贝叶斯方法,具有检测优势,可得到稳定的检测结果。
The power monitoring system is established based on OPC technology,and the network security risk early warning platform of the system is built by combining honeypot and honeynet technology.The network security situation awareness and risk early warning task of the monitoring system are carried out through the construction of 3D warning grid model and the application of 3D convolutional neural network method.The test results show that,the communication efficiency of the power monitoring system designed in this paper can be guaranteed whether it is between instruments and OPC server,or between OPC client and OPC server.In addition,the detection and warning accuracy,precision,recall rate and false positive of the method used in the network security risk early warning platform of power monitoring system are higher than those of support vector machine and naive Bayes method,which has detection advantages and can obtain stable detection results.
作者
刘民
LIU Min(Guangzhou Power Supply Bureau of Guangdong Power Gird,Guangzhou 510410,China)
出处
《信息技术》
2022年第4期180-187,共8页
Information Technology
