基于改进机器学习的电力监控网络准入控制系统  

Power monitoring network access control system based on improved machine learning

作　　者：王依云 吴昊 赖宇阳 冯国聪 张丽娟 WANG Yiyun;WU Hao;LAI Yuyang;FENG Guocong;ZHANG Lijuan(Network Security Division of Platform Security Branch of China Southern Power Grid Digital Grid Research Institute Co.,Ltd.,Guangzhou 510000,China)

机构地区：[1]南方电网数字电网研究院有限公司平台安全分公司网络安全事业部,广东广州510000

出　　处：《电子设计工程》2023年第6期185-188,193,共5页Electronic Design Engineering

摘　　要：传统电力监控网络准入控制系统的数据阻塞率较高,对数据的有效拦截率较低。为解决上述问题,基于改进机器学习技术,深入研究了网络准入控制系统,对硬件结构和软件进行设计。硬件设计包括中央处理器、控制器和无线通信设备,通过确定适应度函数、指标集初始化、选择优秀个体、选择评价指标集群、进化迭代和数据控制实现软件流程。实验结果表明,所设计系统能够有效实现数据阻塞,提高有效拦截率。The traditional admission control system of power monitoring network has high data blocking ability and low effective blocking rate. To solve the above problems,based on the improved machine learning technology,the network admission control system is deeply studied,and the hardware structure and software are designed. The hardware mainly includes CPU,controller and wireless communication equipment. The software process is realized by determining fitness function,initializing index set,selecting excellent individuals,selecting evaluation index cluster,evolutionary iteration and data control.Experimental results show that the designed system can effectively block data and improve the effective interception rate.

关 键 词：改进机器学习 电力监控 网络准入控制 控制系统 

分 类 号：TN301[电子电信—物理电子学]