The Effect of Key Nodes on theMalware Dynamics in the Industrial Control Network

As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.

A New Intrusion Detection Algorithm AE-3WD for Industrial Control Network

In this paper,we propose a intrusion detection algorithm based on auto-encoder and three-way decisions(AE-3WD)for industrial control networks,aiming at the security problem of industrial control network.The ideology of deep learning is similar to the idea of intrusion detection.Deep learning is a kind of intelligent algorithm and has the ability of automatically learning.It uses self-learning to enhance the experience and dynamic classification capabilities.We use deep learning to improve the intrusion detection rate and reduce the false alarm rate through learning,a denoising AutoEncoder and three-way decisions intrusion detection method AE-3WD is proposed to improve intrusion detection accuracy.In the processing,deep learning AutoEncoder is used to extract the features of high-dimensional data by combining the coefficient penalty and reconstruction loss function of the encode layer during the training mode.A multi-feature space can be constructed by multiple feature extractions from AutoEncoder,and then a decision for intrusion behavior or normal behavior is made by three-way decisions.NSL-KDD data sets are used to the experiments.The experiment results prove that our proposed method can extract meaningful features and effectively improve the performance of intrusion detection.

Adaptive Power Control for mutual Interference Avoidance in Industrial Internet-of-Things

With the vigorous development of the Internet of Things and 5G technology, such as machine-to-machine and device-todevice, all kinds of data transmission including environmental monitoring and equipment control strengthens the key role of wireless sensor networks in the large-scale wireless communication system. However, especially in the complex industrial wireless applications, the low utilization efficiency of the limited wireless radio resource enhances the coexistence problem between heterogeneous networks. In this paper, from the severe mutual interference point of view, a mathematical model regarding cumulative interferences in the industrial wireless sensor networks is described. Then, from the perspective of mutual interference avoidance, an adaptive power control scheme is proposed in order to handle the normal communication needs on both the primary link and the secondary link. At last, nonlinear programming is taken to solve the corresponding optimization problem. Some typical analyses are given to verify the effectiveness of the proposed scheme on optimizing the tradeoff between the system throughput and energy consumption. Especially, the energy-efficiency of the novel scheme for Industrial Internet of Things is also analysed. Results show that the proposed power control is efficient. The throughput could be enhanced and the energy consumption could be reduced with the guarantee of mutual interference avoidance.

Identifying Industrial Control Equipment Based on Rule Matching and Machine Learning

To identify industrial control equipment is often a key step in network mapping,categorizing network resources,and attack defense.For example,if vulnerable equipment or devices can be discovered in advance and the attack path canbe cut off,security threats canbe effectively avoided and the stable operationof the Internet canbe ensured.The existing rule-matching method for equipment identification has limitations such as relying on experience and low scalability.This paper proposes an industrial control device identification method based on PCA-Adaboost,which integrates rule matching and machine learning.We first build a rule base from network data collection and then use single andmulti-protocol rule-matchingmethods to identify the type of industrial control devices.Finally,we utilize PCA-Adaboost to identify unlabeled data.The experimental results show that the recognition rate of this method is better than that of the traditional Nmap device recognitionmethod and the device recognition accuracy rate reaches 99%.The evaluation effect of the test data set is significantly enhanced.

Wireless Fault-Tolerant Controllers in Cascaded Industrial Workcells Using Wi-Fi and Ethernet

A Wireless Networked Control System using 802.11b is used to model fault-tolerance at the controller level of an industrial workcell. The fault-tolerance study in this paper presents the cascading of two independent workcells where each controller must be able to handle the load of both cells in case of failure of the other one. The intercommunication is completely wireless between the cells and this feature is investigated. The model incorporates unmodified 802.11b and 802.11g for communication. Sensors send sampled data to both controllers and the controllers to exchange a watchdog. The fault-free and faulty models are both simulated using OPNET Network Modeler. External interference on the critical intercommunication link is also investigated. Results of simulations are presented based on a 95% confidence analysis, guaranteeing correct system performance.

Time-stamped predictive functional control for networked control systems with random delays

The random delays in a networked control system (NCS) degrade control performance and can even destabilize the control system.To deal with this problem,the time-stamped predictive functional control (PFC) algorithm is proposed,which generalizes the standard PFC algorithm to networked control systems with random delays.The algorithm uses the time-stamp method to estimate the control delay,predicts the future outputs based on a discrete time delay state space model,and drives the control law that applies to an NCS from the idea of a PFC algorithm.A networked control system was constructed based on TrueTime simulator,with which the time-stamped PFC algorithm was compared with the standard PFC algorithm.The response curves show that the proposed algorithm has better control performance.

CAGCN:Centrality-Aware Graph Convolution Network for Anomaly Detection in Industrial Control Systems

In industrial control systems,the utilization of deep learning based methods achieves improvements for anomaly detection.However,most current methods ignore the association of inner components in industrial control systems.In industrial control systems,an anomaly component may affect the neighboring components;therefore,the connective relationship can help us to detect anomalies effectively.In this paper,we propose a centrality-aware graph convolution network(CAGCN)for anomaly detection in industrial control systems.Unlike the traditional graph convolution network(GCN)model,we utilize the concept of centrality to enhance the ability of graph convolution networks to deal with the inner relationship in industrial control systems.Our experiments show that compared with GCN,our CAGCN has a better ability to utilize this relationship between components in industrial control systems.The performances of the model are evaluated on the Secure Water Treatment(SWaT)dataset and the Water Distribution(WADI)dataset,the two most common industrial control systems datasets in the field of industrial anomaly detection.The experimental results show that our CAGCN achieves better results on precision,recall,and F1 score than the state-of-the-art methods.

Cloud control for IIoT in a cloud-edge environment

The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.

基于ControlLogix的PLC冗余控制系统设计

从Rockwell Automation工业控制网络的架构以及组网方式出发,阐述了基于ControlLogix5550控制器的PLC冗余控制系统的实现过程。

基于ControlLogix系统的电梯多级控制网络的应用研究

采用基于罗克韦尔ControlLogix系统集成架构作为控制核心,以电梯作为被控对象,通过controllogix机架下的EtherNe(t以太网)、Contro|Ne(t控制网)和DeviceNe(t设备网)组成多级控制网络进行现场信号传输。实现多级网络控制电梯的要求,弥补PLC控制电梯常规设计方法的缺陷。

Estimation of Hammerstein nonlinear systems with noises using filtering and recursive approaches for industrial control

This paper discusses a strategy for estimating Hammerstein nonlinear systems in the presence of measurement noises for industrial control by applying filtering and recursive approaches.The proposed Hammerstein nonlinear systems are made up of a neural fuzzy network(NFN)and a linear state`-space model.The estimation of parameters for Hammerstein systems can be achieved by employing hybrid signals,which consist of step signals and random signals.First,based on the characteristic that step signals do not excite static nonlinear systems,that is,the intermediate variable of the Hammerstein system is a step signal with different amplitudes from the input,the unknown intermediate variables can be replaced by inputs,solving the problem of unmeasurable intermediate variable information.In the presence of step signals,the parameters of the state-space model are estimated using the recursive extended least squares(RELS)algorithm.Moreover,to effectively deal with the interference of measurement noises,a data filtering technique is introduced,and the filtering-based RELS is formulated for estimating the NFN by employing random signals.Finally,according to the structure of the Hammerstein system,the control system is designed by eliminating the nonlinear block so that the generated system is approximately equivalent to a linear system,and it can then be easily controlled by applying a linear controller.The effectiveness and feasibility of the developed identification and control strategy are demonstrated using two industrial simulation cases.

Control-Net网络技术在污水处理厂的应用

Control -net网络是一个开放的、高速的、确定性的工业局域网,用于传输对时间有苛刻要求的信息,为对等通信提供实时控制和报文传送。可实现PC机、控制器、操作界面设备、I/O模块等不同设备间的联网通信。网络成功地应用在多种工业自动控制系统上。

Identifying and Verifying Vulnerabilities through PLC Network Protocol and Memory Structure Analysis

Cyberattacks on the Industrial Control System(ICS)have recently been increasing,made more intelligent by advancing technologies.As such,cybersecurity for such systems is attracting attention.As a core element of control devices,the Programmable Logic Controller(PLC)in an ICS carries out on-site control over the ICS.A cyberattack on the PLC will cause damages on the overall ICS,with Stuxnet and Duqu as the most representative cases.Thus,cybersecurity for PLCs is considered essential,and many researchers carry out a variety of analyses on the vulnerabilities of PLCs as part of preemptive efforts against attacks.In this study,a vulnerability analysis was conducted on the XGB PLC.Security vulnerabilities were identified by analyzing the network protocols and memory structure of PLCs and were utilized to launch replay attack,memory modulation attack,and FTP/Web service account theft for the verification of the results.Based on the results,the attacks were proven to be able to cause the PLC to malfunction and disable it,and the identified vulnerabilities were defined.

Network DDE在Client/Server结构工控软件中的应用

本文介绍了ＮｅｔｗｏｒｋＤＤＥ的原理，并给出了用ＮｅｔｗｏｒｋＤＤＥ作为中间件应用在具有Ｃｌｉｅｎｔ／Ｓｅｒｖ－ｅｒ结构的机器人装配线监控系统的具体例子。

RRCNN: Request Response-Based Convolutional Neural Network for ICS Network Traffic Anomaly Detection

Nowadays,industrial control system(ICS)has begun to integrate with the Internet.While the Internet has brought convenience to ICS,it has also brought severe security concerns.Traditional ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security experts.They are not aimed at the original network data,nor can they capture the potential characteristics of network packets.Therefore,the following improvements were made in this study:(1)A dataset that can be used to evaluate anomaly detection algorithms is produced,which provides raw network data.(2)A request response-based convolutional neural network named RRCNN is proposed,which can be used for anomaly detection of ICS network traffic.Instead of using statistical features manually extracted by security experts,this method uses the byte sequences of the original network packets directly,which can extract potential features of the network packets in greater depth.It regards the request packet and response packet in a session as a Request-Response Pair(RRP).The feature of RRP is extracted using a one-dimensional convolutional neural network,and then the RRP is judged to be normal or abnormal based on the extracted feature.Experimental results demonstrate that this model is better than several other machine learning and neural network models,with F1,accuracy,precision,and recall above 99%.

NETWORK 6000 DCS与工控机串行通讯的实现方法

本文介绍了 NETWORK 6000 DCS与工控机之间串行通讯的系统结构、硬件配置、通讯协议和软件编程方法。重点介绍了 Modbus通讯协议，给出了系统组态过程及主要通讯程序。这种通讯方式同样可以解决其他使用 MODBUS协议的设备（如支持该协议的 PLC控制器）之间通讯问题。

ControlLogix系统DLR网络在选矿悬浮焙烧炉控制系统的应用研究

酒钢选矿悬浮磁化焙烧项目是攻克世界级难选铁矿石高效利用难题的重点工程项目,悬浮焙烧炉应用了罗克韦尔公司先进的Control Logix控制系统,通过Ether Net/IP工业标准网络技术构建Control Logix系统DLR网络架构,实现了悬浮焙烧生产过程自动化控制的高产、高效、稳定运行。DLR网络强大的状态监测功能,使诊断更简单、排错更快捷,有效管理全厂的实时控制和信息。本文从DLR环形网络工作原理、悬浮磁化焙烧炉控制系统、DLR网络的配置、DLR网络监控功能实现的方式方法及应用实例等多方面进行了应用分析。

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.

半定量信息工业控制异构网络安全评估

工业控制系统的异构性及其与互联网深度融合给工控系统安全评估带来精度不高等诸多问题.本文提出一种基于半定量信息的工业异构网络安全态势评估模型,该模型可以提高评估精度,解决难以描述不确定性信息和难以契合工控异构网络复杂结构等现存问题.该方法将工业控制网络划分为不同异构子网,根据子网特点选取有针对性的评估属性,并使用性能优秀的ER规则对不同类型的评估属性进行融合,可以有效利用定性知识和定量数据,获得更加准确的评估结果.在多个实际工业控制网络数据集上进行评估实验得到的结果表明:本文提出的模型与网络安全评估实际参考值的均方根误差值比RF、BP、SVM模型分别降低了80.2%、40.8%和87.7%,验证了该模型的有效性和准确性.

化工园区事故情景耦合演化的复杂网络分析

为进一步研究化工园区事故易因耦合效应引发事故升级等问题,首先,以化工园区储存环节为研究对象,从要素及关系2个角度构建涵盖人因层、物质层、环境层、管理层、组合层及事故层的典型事故情景要素框架体系。然后,基于拓扑理论及相关方法,构建化工园区典型事故情景复杂网络模型,分析复杂网络的统计特征,确定节点的网络贡献度和连接边的脆弱性,探究最大可能的致因路径,并结合事故的演化特征提出断链视角下的应急响应对策。最后,引入案例分析其最大致因路径,并提出相关事故的预防措施。研究结果表明:初期是化工园区事故控制的关键时期,加强员工培训是强化初期处置的重要手段之一,点火源是事故耦合升级的关键因素之一;本文构建的化工园区事故情景演化复杂网络模型具有较好的实用性,案例分析得到的最大致因路径与实际情况基本相符。研究结果可为化工园区的事故防控及演化控制提供一定参考。