基于时间窗切分和深度神经网络的造纸生产线工控网络安全态势感知
Security Situation Awareness of Industrial Control Networks in Paper Production Lines Based on Time Window Segmentation and Deep Neural Networks
代冬凤 1陈岩岩1
作者信息
- 1. 九州职业技术学院 机电与汽车工程学院,江苏 徐州,221116
- 折叠
摘要
造纸生产线的工控网络需要外接大量的传感器,其具有工控系统和传感网络的双重属性,对其安全态势必须考虑工控系统的时间窗属性和传感器的网络属性,提出基于时间窗切分和深度神经网络的造纸生产线工控网络安全态势感知方法.首先,采集造纸生产线工控网络系统的安全态势数据,并结合时间窗切分方法与主成分分析法,对采集的数据展开数据处理与分析;其次,采用深度神经网络中的卷积神经网络为决策工具,通过前向传播与反向传播识别造纸生产线工控网络攻击;最后,根据识别结果构建网络安全态势感知模型,对造纸生产线工控网络安全态势展开感知,完成造纸生产线工控网络安全态势感知.实验结果表明:所提方法在造纸行业工控网络中具有较高的网络安全态势感知能力和感知准确性,适用于感知造纸生产线工控网络安全态势.
Abstract
The industrial control network of paper production line requires a large number of external sensors,which have the dual attributes of industrial control system and sensor network.The security situation of the industrial control system must consider the time window attribute and sensor network attribute.A paper production line industrial control network security situation awareness method based on time window segmentation and deep neural network is proposed.Firstly,collect security situation data of the industrial control network system of the paper production line,and combine time window segmentation method and principal component analysis method to process and analyze the collected data;Secondly,convolutional neural networks in deep neural networks are used as decision tools to identify industrial control network attacks on paper production lines through forward and backward propagation;Finally,based on the recognition results,a network security situation awareness model is constructed to perceive the security situation of the industrial control network in the paper production line,and complete the perception of the security situation of the industrial control network in the paper production line.The experimental results show that the proposed method has a high ability and accuracy in perceiving network security situations in the industrial control network of the paper industry,and is suitable for perceiving the security situation of industrial control networks in paper production lines.
关键词
造纸生产线/安全态势/时间窗切分/深度神经网络/主成分分析法Key words
paper production line/network security situation/time window segmentation/deep neural network/principal component analysis method引用本文复制引用
基金项目
九州职业技术学院院级教科研项目(Yjz202003)
出版年
2024
NETL
NSTL
万方数据