Research and implementation of vulnerability detection system for power plant industrial control network

2021 ◽  
Author(s):  
Xiao Zhang ◽  
Yang Pan ◽  
Cong Li ◽  
Zengkai Wang
Keyword(s):  
Power Plant ◽  
Detection System ◽  
Control Network ◽  
Vulnerability Detection ◽  
Industrial Control

scholarly journals Industrial Control Malicious Traffic Anomaly Detection System Based on Deep Autoencoder

2021 ◽  
Vol 8 ◽  
Author(s):  
Weiping Wang ◽  
Chunyang Wang ◽  
Yongzhen Guo ◽  
Manman Yuan ◽  
Xiong Luo ◽  
...  
Keyword(s):  
Detection System ◽  
Control Process ◽  
Principal Component ◽  
Component Analysis ◽  
Control Network ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
Data Dimensionality Reduction ◽  
Leibler Divergence ◽  
Value Decomposition

Industrial control network is a direct interface between information system and physical control process. Due to the lack of authentication, encryption, and other necessary security protection designs, it has become the main target of malicious attacks under the trend of increasing openness. In order to protect the industrial control systems, we examine the detection of abnormal traffic in industrial control network and propose a method of detecting abnormal traffic in industrial control network based on autoencoder technology. What is more, a new deep autoencoder model was designed to reduce the dimensionality of traffic data in industrial control network. In this article, the Kullback–Leibler divergence was added to the loss function to improve the ability of feature extraction and the ability to recover raw data. Finally, this model was compared with the traditional data dimensionality reduction method (principal component analysis (PCA), independent component analysis, and singular value decomposition) on gas pipeline dataset. The results show that the approach designed in this article outperforms the three methods in different scenes in terms of f1 score.

Attack Graph Generation and Visualization for Industrial Control Network

2020 ◽  
Author(s):  
Yanli Feng ◽  
Gongliang Sun ◽  
Zhiyao Liu ◽  
Chenrui Wu ◽  
Xiaoyang Zhu ◽  
...  
Keyword(s):  
Control Network ◽  
Industrial Control ◽  
Attack Graph ◽  
Graph Generation

Design of Energy-Saving Control Software Algorithms in Large Industrial Network

2014 ◽  
Vol 644-650 ◽  
pp. 828-831
Author(s):  
Wen Lai Liu
Keyword(s):  
Energy Saving ◽  
Control Network ◽  
Fuzzy Pid Control ◽  
Industrial Control ◽  
Genetic Method ◽  
Software Algorithms ◽  
Operation Process ◽  
Industrial Network ◽  
Energy Saving Control ◽  
Industrial Control Networks

In the operation process of large industrial control network, with conventional fuzzy PID control algorithm for industrial control networks energy-saving control, excessive industrial networks will aggravate machine wear of the single network, thereby reduce the effect of energy-saving for industrial network. This paper presents an approach for industrial network energy-saving control based on non-uniform data production rate. According to the relationship between the network load and loss, the loss model of industrial control network can be established. Adaptive linear genetic method is utilized to control industrial control network energy-saving load, so as to achieve energy-saving control of industrial control network. Experimental results show that the algorithm can effectively improve the energy-saving efficiency of industrial control network, and achieve satisfactory results.

scholarly journals A novel industrial control architecture based on Software-Defined Network

2018 ◽  
Vol 51 (7-8) ◽  
pp. 360-367
Author(s):  
Geng Liang ◽  
Wen Li
Keyword(s):  
Large Scale ◽  
Service Management ◽  
Virtual Machines ◽  
Industrial Process ◽  
Control Network ◽  
Software Defined Network ◽  
Intelligent Network ◽  
Industrial Control ◽  
Control Functions ◽  
And Performance

Traditionally, routers and other network devices encompass both data and control functions in most large enterprise networks, making it difficult to adjust the network infrastructure and operation to large-scale addition of end systems, virtual machines, and virtual networks in industrial comprehensive automation. A network organizing technique that has come to recent prominence is the Software-Defined Network (SDN). A novel SDN based industrial control network (SDNICN) was proposed in this paper. Intelligent network components are included in a SDNICN. Switches in SDNICN provided fundamental network interconnection for the whole industrial control network. Network controller is used for data transmission, forwarding and routing control between different layers. Service Management Center (SMC) is essentially responsible for managing various services used in industrial process control. SDNICN can not only greatly improve the flexibility and performance of industrial control network but also meet the intelligence and informatization of the future industry.

scholarly journals Analysis and Application of intelligent Power Control System Cyber Security Situation Awareness Based on Wavelet Neural Network

2021 ◽  
Vol 2078 (1) ◽  
pp. 012067
Author(s):  
Jingcheng Zhao ◽  
Xiaomeng Li ◽  
Yaofu Cao ◽  
Junwen Liu ◽  
Junlu Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Network Security ◽  
Power Control ◽  
Situation Awareness ◽  
Wavelet Neural Network ◽  
Control Network ◽  
Industrial Control ◽  
True Value ◽  
Power Control System

Abstract In recent years, international industrial control network security incidents have occurred frequently. As a core component of the industrial control field, intelligent power control systems are increasingly threatened by external network attacks. Based on the current research status of power industrial control network security, closely combining the development of active monitoring and defense technology in the public network field and the problems encountered by network security operators in actual work, this paper uses data mining methods to study the power control system network security situation awareness technology. Combing operational data collection and integrated processing, situation index screening and extraction, we use wavelet neural network analysis method to train the sampled data set, and finally calculate the true value of the network security status through deep intelligent learning. Finally, we conclude that the artificial intelligence algorithm based on wavelet neural network can be used for power control system network security situation awareness. In actual work, it can predict the situation value for a period of time in the future and assist network security personnel in judgment and decision-making.

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