煤炭企业工控网络安全防护与预测方法研究

Title

Study on safety protection and prediction method of industrial control network in coal enterprises
作者

顾闯
Author

GU Chuang
单位

煤炭科学技术研究院有限公司煤矿应急避险技术装备工程研究中心北京市煤矿安全工程技术研究中心
Organization

1.China Coal Research Institute Co.,Ltd.,Beijing ,China;2.Engineering Research Center For Technology Equipment of Emergency Refuge in Coal Mine，Beijing ,China;3.Beijing Mine Safety Engineering Technology Research Center，Beijing ，China
摘要

针对煤矿企业工控网络的安全防护和预警在煤矿安全生产中的重要性，分析了煤矿工控网络安全防护的3个主要区域：井上安全防护体系、井下传输通信、井下控制执行及相关安全防护策略，并提出在数字监控系统网络安全防护中，尝试引入大数据分析的思路，应用LM神经网络预测方法，利用网络数据的包方向、包间隔、CAN协议数据位为体征信息集合，建立网络异常预测系统的架构和模型程序设计。并通过试验结果表明：通过对3层神经网络的隐含层神经元数量由3个分别增加到5个和7个之后，该模型趋近稳定、预测精度误差趋近0.007%，能够较好地对监控系统网络异常做出分类，以实现预测预警功能。
Abstract

According to important of safety protection and early warning of industrial control network safety production in coal mine，this paper analyzes three main areas of coal mine industrial control network security protection:the safety protection system on the well,the underground transmission and communication,the implementation of underground control and related safety protection strategies.In the network security protection of digital monitoring system,we try to introduce the idea of large data analysis,using the LM neural network prediction method,using the packet direction of network data,the packet interval,the data bit of the CAN protocol as collection of information,and establish the architecture and model program design of the network anomaly prediction system.The experimental results show that trough increasing the number of hidden layer neurons in the 3 layer neural network from 3 to 5 and 7,the model is stable and the error of prediction accuracy is close to 0.007%.It can classify the network anomaly of the monitoring system well to realize the prediction and early warning power.
关键词

工控网络安全防护 LM神经网络网络体征煤矿
KeyWords

industrial control net;safety protection;LM neural network;network signs；coal mine
基金项目(Foundation)

中国煤炭科工集团有限公司科技创新基金面上资助项目（2017MS002）
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