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.

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.

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.

A Controller-Based Architecture for Information Centric Network Construction and Topology management

Information-Centric Networking(ICN) has recently emerged as a result of the increased demand to access contents regardless of their location in the network services. This new approach facilitates content distribution as a service of the network with lower delay and higher security in comparison with the current IP network. Applying ICN in current IP infrastructure leads to major complexities. One approach to deploy ICN with less complexity is to integrate ICN with Software Defined Networking(SDN). The SDN controller manages the content distribution, caching, and routing based on the users' requests. In this paper, we extend these context by addressing the ICN topology management problem over the SDN network to achieve an improved user experience as well as network performance. In particular, a centralized controller is designed to construct and manage the ICN overlay. Experimental results indicate that this adopted topology management strategy achieves high performance, in terms of low failure in interest satisfaction and reduced download time compared to a plain ICN.

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.

Smart control plane for information centric network-internet service provider networks

The information centric network(ICN)has been widely discussed in current researches.The ICN interoperation with a traditional IP network and caching methods are one of the research topics of interest.For economic reasons,the capability of applying the ICN to internet service providers(ISPs)with various traditional IP protocols already implemented,especially IGP,MPLS,VRF,and TE,does not require any change on the IP network infrastructure.The biggest concern of ISPs is related to their customers’contents delivery speed.In this paper,we consider ICN caching locations in ISP by using the concept of locator/ID separation protocol(LISP)for interoperation between a traditional IP address and name-based ICN.To be more specific,we propose a new procedure to determine caching locations in the ICN by using the cuckoo search algorithm(CSA)for finding the best caching locations of information chunks.Moreover,we create the smart control plane(SCP)scheme which is an intelligent controlling,managing,and mapping system.Its function is similar to the software defined network concept.We show how the proposed SCP system works in both synthetic small network and real-world big network.Finally,we show and evaluate the performance of our algorithm comparison with the simple search method using the shortest path first algorithm.

Integrated Architecture for Networking and Industrial Internet Identity

Several excellent works have been done on the industrial Internet;however,some problems are still ahead,such as reliable security,heterogeneous compatibility,and system efficiency.Information-Centric Networking(ICN),an emerging paradigm for the future Internet,is expected to address the challenges of the industrial Internet to some extent.An integrated architecture for industrial network and identity resolution in the industrial Internet is proposed in this paper.A framework is also designed for the ICN-based industrial Network And Named Data Networking(NDN)based factory extranet with Software-Defined Networking(SDN).Moreover,an identity resolution architecture in the industrial Internet is proposed based on ICN paradigms with separate resolution nodes or with merging resolution and routing.

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.

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.

面向无线工业网络的数据链路层协议WICN-Z

首先介绍了工业现场控制对无线通信网络WICN的具体要求,针对其要求分析比较了目前几种常用的无线网络MAC协议的特点,提出了一种基于令牌且能利用目前被普遍使用的CSMA协议的数据链路层控制机制,简称WICN-Z协议。详细叙述了WICN-Z的通信协议栈的结构、主站的定义、令牌环的动态建立和复原过程、各个移动站点的动态加入与离开过程等,定义了几种用到的帧的结构。最后利用OMNet++平台对WICN-Z和CSMA/CA性能进行了仿真比较,并且在基于GAINS的硬件平台上实现了该协议,且在一个工厂车间现场建立了包括9个移动1个固定站点的一个网络系统,针对数据吞吐量和响应时间指标作了初步的测试。仿真结果和实际测试显示,WICN-Z确保了无线信道接入的公平性,其数据吞吐量大于CSMA/CA,且减少了数据的传输延迟。

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。

工控入侵检测系统安全增强方案

针对基于联邦学习工控入侵检测模型训练时存在中心化、易受攻击的问题,提出一种去中心安全增强方案。在联邦学习训练中,通过模型参数确定参数聚合临时服务器并引入跳变机制,可以有效抵御拒绝服务攻击;利用系统流量数据中时间戳等信息,减缓重放攻击的影响;提出一种基于同态加密的流量加密方案。实验结果表明,所提方案可以抵抗每秒10000数据包的拒绝服务攻击,提高了联邦学习的安全性。

基于动态贝叶斯博弈的工业控制网络恶意接入检测研究

针对工业控制网络(Industrial Control Network, ICN)远程接入场景下未经授权访问、拒绝服务攻击、欺骗攻击以及信息披露等安全问题,通过STRIDE威胁建模方法对该场景下的潜在威胁进行分析,提出一种基于动态贝叶斯博弈的接入检测框架。该方法能够将试图接入ICN的非法、恶意请求筛选出来并阻断,同时利用持续进行的多轮博弈迭代以及SDN灵活动态的特性对策略参数进行实时调整,以防止相同恶意接入源的再次访问。仿真实验结果表明,随着博弈轮数的增加,相比于现有的两类恶意接入防御方法,该框架的检测准确性提升了3%以上,假阳性比例下降了1.2%以上,检测效率提升了14.7%以上,且具有较好的鲁棒性。

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.

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。

基于孪生网络的分类器输出重复性优化方法

在工业表面质检场景中,深度分类神经网络常用于对产品图像进行分类,实现对产品的合格判别或品质分级,搭载深度分类神经网络的表面质检设备需进行量具的检验重复性与再现性(AR&R)评估。但产品载具受装配公差以及设备振动等因素的影响,导致设备拍摄的产品图像会出现位置、角度、亮度、模糊度的相关扰动。针对扰动图像,分类神经网络将无法输出一致的分类结果和分类概率,使得表面质检设备无法通过AR&R评估,将此问题总结为网络输出重复性问题。为此,提出一种基于孪生网络的分类神经网络训练方法。孪生主网络使用原始样本进行监督学习训练,学习输出正确的分类类别,孪生次网络通过指数平滑拷贝主网络权重,输出与原始样本对应的扰动样本的特征嵌入,用于对主网络进行对比学习训练,使主网络对原始样本与扰动样本的输入均输出一致的分类概率,在推理过程中仅保留主网络用于产品缺陷分类。实验结果表明,该方法的分类准确率和分类概率方差分别为99.3462%和0.001016,可有效缓解使用深度分类神经网络对工业产品图像分类的输出重复性问题,在显著降低分类概率方差的同时分类准确率也有一定提升。

电力工控网络0day漏洞风险自动识别技术

为了准确识别漏洞风险,提出一种电力工控网络0day漏洞风险自动识别方法。将电力工控网络根据需求转换为二维数据,经过归一化处理获取灰度矩阵,采用二维小波阈值去噪方法对数据去噪处理。将常规序列特征提取规则作为基本单元,采用特征提取方法提取电力工控网络0day漏洞风险特征,构建特征集合,将集合中的全部数据映射到二维平面上,通过维诺图区分数据,实现电力工控网络0day漏洞风险自动识别。实验结果表明,所提方法可以有效提升漏洞风险自动识别结果的准确性,同时还能够有效缩短识别时间。

基于深度学习的自适应控制算法在工业机器人中的应用研究

随着工业自动化技术的迅速发展,人们对工业机器人控制系统的需求日益增加。传统控制系统存在环境适应性和灵活性不足等问题。基于此,文章提出了一种基于深度学习的自适应控制算法,并在工业机器人上进行了应用。该算法通过结合卷积神经网络(CNN)和强化学习(RL),使得机器人能够实时学习并适应动态变化的操作环境。实验结果显示,相较于传统算法,该算法在任务执行效率和适应性方面有显著提升。