基于深度学习的工控网络安全立体化防御体系研究  

Research on a Three-dimensional Defense System for Industrial Control Network Security Based on Deep Learning

作　　者：周玉锁 ZHOU Yusuo(The 54th Research Institute of CETC,Shijiazhuang 050081,China)

机构地区：[1]中国电子科技集团公司第五十四研究所,河北石家庄050081

出　　处：《计算机与网络》2025年第1期45-49,共5页Computer & Network

摘　　要：随着信息技术的不断发展,工业控制(简称工控)网络已经成为现代工业生产中不可或缺的组成部分,同时工控系统所面临的网络安全威胁也更加严峻。传统的安全防御手段已经无法满足工控系统安全防护的需求,因此设计一种基于深度学习的工控网络安全立体化防御体系是十分必要的。针对工控网络安全问题进行深入研究,提出了一种基于深度学习的工控网络安全立体化防御体系,包括网络监控、数据分析和安全防御三部分。其中,网络监控部分主要通过深度学习模型对网络流量进行监控和异常检测;数据分析部分利用深度学习模型对采集的工控数据进行分析,从而挖掘出潜在的安全威胁;安全防御部分则结合深度学习和传统的安全防御手段,提供一种全方位的工控网络安全防御体系。With the continuous development of information technology,the industrial control network has become an indispensable part of modern industrial production,and the network security threats faced by industrial control systems have become more severe.Traditional security defense means can no longer meet the needs of industrial control system security protection,so it is necessary to design a three-dimensional defense system for industrial control network security based on deep learning.The in-depth research on industrial control network security issues is conducted first,and a three-dimensional defense system for industrial control network security based on deep learning is proposed,including three aspects of network monitoring,data analysis and security defense.Among them,the monitoring and the abnormal detection of network traffic are done by the network monitoring part through deep learning models;deep learning models are also used by the data analysis part to analyze the collected industrial control data,so as to find potential security threats;a comprehensive industrial control network security defense system is provided by the security defense part by combining deep learning and traditional security defense means.

关 键 词：工控网络安全 深度学习 立体化防御 网络监控 数据分析 安全防御 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]