基于深度学习算法的造纸企业工控网络安全管理模型研究
Research on Industrial Control Network Security Management Model of Paper Enterprise Based on Deep Learning Algorithm
乔少华 1祝玲 1张翠玲1
作者信息
- 1. 榆林市开放大学,陕西榆林,719000
- 折叠
摘要
工业控制系统通常应用于化工、电力和造纸等诸多行业.随着信息技术的不断升级和工业控制系统的逐步完善,企业工控网络的安全越来越受重视.基于此,简述了造纸企业工控网络所存在的种种安全隐患,重点基于深度学习算法,结合异常流量检测对造纸企业工控网络的安全管理问题展开研究,提出一种多尺度跳跃激励网络结构对卷积神经网络进行优化,构建了工控网络安全管理模型,并使用KDD CUP 99数据集进行试验验证,该模型能够对工控网络中的异常流量进行深度检测,且准确率比普通模型更高.
Abstract
Industrial control systems are usually used in many industries such as chemical industry,electric power industry and paper industry.With the continuous upgrading of information technology and the gradual improvement of industrial control system,the security of enterprise industrial control network is paid more and more attention.Based on this,this paper briefly describes various security risks existing in industrial control networks of papermaking enterprises.It focuses on the research of security management of industrial control networks of papermaking enterprises based on deep learning algorithm combined with abnormal traffic detection,proposes a multi-scale jump incentive network structure to optimize convolutional neural networks,and constructs an industrial control network security management model.The KDD CUP 99 data set is used to verify that the model can detect abnormal traffic in industrial control network in depth,and the accuracy is higher than that of the common model.
关键词
深度学习/卷积神经网络/造纸企业/工控网络/安全管理Key words
deep learning/convolutional neural network/paper making enterprises/industrial control network/safety management引用本文复制引用
基金项目
陕西开放大学教育教学改革研究项目(2023)(Sxkd2023zx16)
出版年
2024
NETL
NSTL
万方数据