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.

Self-Triggered Set Stabilization of Boolean Control Networks and Its Applications

Set stabilization is one of the essential problems in engineering systems, and self-triggered control(STC) can save the storage space for interactive information, and can be successfully applied in networked control systems with limited communication resources. In this study, the set stabilization problem and STC design of Boolean control networks are investigated via the semi-tensor product technique. On the one hand, the largest control invariant subset is calculated in terms of the strongly connected components of the state transition graph, by which a graph-theoretical condition for set stabilization is derived. On the other hand, a characteristic function is exploited to determine the triggering mechanism and feasible controls. Based on this, the minimum-time and minimum-triggering open-loop, state-feedback and output-feedback STCs for set stabilization are designed,respectively. As classic applications of self-triggered set stabilization, self-triggered synchronization, self-triggered output tracking and self-triggered output regulation are discussed as well. Additionally, several practical examples are given to illustrate the effectiveness of theoretical results.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

The Deformation Analysis of the 3D Alignment Control Network Based on the Multiple Congruence Models

In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.

Preliminary Design of Control Network for HT-7U Tokamak Cryogenic System

In the course of the cryoplant modernization, a control network will be set up in order to facilitate the control, the supervision, the centralized data acquisition and the alarm handling of the cryogenic system for HT-7U tokamak. The paper introduces the preliminary design of control network based on the Controller Link Network for HT-7U tokamak cryogenic system. The multi-layer structure mentioned in the paper is the mainstream of automatic control. The control philosophy, the structure of the network and the components for control are also presented.

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.

Stabilization of multiple independent linear systems with control networks

The problem of stabilizing multiple independent linear systems sharing one common network cable is presented and solved. Both the quantization and time sequencing are studied in the field of control over networks by providing the formulated stabilizing sufficient condition which illustrates the relationship between the system instability, quantization and time sequencing,and the data rate is also presented in terms of the quantization and time sequencing. A numerical example is given to illustrate the result.

A Method for Surveying Control Network Optimization Based on Reliability Properties

Surveying control network optimization design is related to standards, such as precision, reliability, sensitivity and the cost, and these standards are related closely to each other. A new method for surveying control network simulation optimization design is proposed. This method is based on the inner reliability index of the observation values.

Applications of Graph Theory to Gross Error Detection for GPS Geodetic Control Networks

This paper describes a broad perspective of the application of graph theory to establishment of GPS control networks whereby the GPS network is considered as a connected and directed graph with three components.In this algorithm the gross error detection is undertaken through loops of different spanning trees using the "Loop Law" in which the individual components Δ X, Δ Y and Δ Z sum up to zero.If the sum of the respective vector components ∑X,∑Y and ∑Z in a loop is not zero and if the error is beyond the tolerable limit (ε>w),it indicates the existence of gross errors in one of the baselines in the loop and therefore the baseline must be removed or re_observed.After successful screening of errors by graph theory,network adjustment can be carried out.In this paper,the GPS data from the control network established as reference system for the HP Dam at Baishan county in Liaoning province is presented to illustrate the algorithm.

Context-Aware System Modeling Based on Boolean Control Network

Boolean control network consists of a set of Boolean variables whose state is determined by other variables in the network. Boolean network is used for modeling complex system. In this paper, we have presented a model of a context-aware system used in smart home based on Boolean control networks. This modeling describes the relationship between the context elements (person, time, location, and activity) and services (Morning Call, Sleeping, Guarding, Entertainment, and normal), which is effective to logical inference. We apply semi tensor matrix product to describe the dynamic of the system. This matrix form of expression is a convenient and reasonable way to design logic control system.

Fuzzy adaptive learning control network with sigmoid membership function

To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.

Chinese Surveying and Control Network for Earth-Orbit Satellites and Deep Space Detection

The relationship between the surveying and contro l network(CSN) for earth-orbit satellite and spatial geodesy, and the relationshi p between the CSN for deep space celestial bodies and detectors, and deep space detection are briefly summarized, and so are the basic technical needs of the de ep space surveying and control network(DSN). Then, the techniques, the constitue nts and the distributing of Chinese satellite CSN (CSCSN) and other radio observ ing establishments in China are introduced. Lastly, with the primary CSCSN and o ther observing establishments, some projects for China to rebuild a more perfect CSCSN, and to establish a DSN are analyzed and stated.

Antidepressants combined with psychodrama improve the coping style and cognitive control network in patients with childhood trauma-associated major depressive disorder

BACKGROUND The use of antidepressant therapy alone has a limited efficacy in patients with childhood trauma-associated major depressive disorder(MDD).However,the effectiveness of antidepressant treatment combined with psychodrama in these patients is unclear.AIM To evaluate the effectiveness of antidepressant treatment combined with psychodrama.METHODS Patients with childhood trauma-associated MDD treated with antidepressants were randomly assigned to either the psychodrama intervention(observation group)or the general health education intervention(control group)and received combination treatment for 6 mo.The observation group received general health education given by the investigator together with the“semi-structured group intervention model”of Yi Shu psychodrama.A total of 46 patients were recruited,including 29 cases in the observation group and 17 cases in the control group.Symptoms of depression and anxiety as well as coping style and resting-state functional magnetic resonance imaging were assessed before and after the intervention.RESULTS Symptoms of depression and anxiety,measured by the Hamilton Depression Scale,Beck Depression Inventory,and Beck Anxiety Inventory,were reduced after the intervention in both groups of patients.The coping style of the observation group improved significantly in contrast to the control group,which did not.In addition,an interaction between treatment and time in the right superior parietal gyrus node was found.Furthermore,functional connectivity between the right superior parietal gyrus and left inferior frontal gyrus in the observation group increased after the intervention,while in the control group the connectivity decreased.CONCLUSION This study supports the use of combined treatment with antidepressants and psychodrama to improve the coping style of patients with childhood trauma-associated MDD.Functional connectivity between the superior parietal gyrus and inferior frontal gyrus was increased after this combined treatment.We speculate that psychodrama enhances the internal connectivity of the cognitive control network and corrects the negative attention bias of patients with childhood trauma-associated MDD.Elucidating the neurobiological features of patients with childhood trauma-associated MDD is important for the development of methods that can assist in early diagnosis and intervention.

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.

Disrupted functional connectivity of default mode network and executive control network in patients with vascular cognitive impairment, no dementia

Objective： To investigate functional connectivity within default mode network （DMN） and ex-ecutive control network （ECN） in vascular cognitive im-pairment, no dementia （VCIND）. Methods： Twenty-eight VCIND patients and sixteen healthy controls were recruit-ed. A seed-based connectivity analysis was performed us-ing data from resting-state functional magnetic resonance imaging （fMRI）. Based on previous fndings, posteriorcingulate cortex （PCC） and dorsolateral prefrontal cortex （DLPFC） were chosen as regions of interest to study these networks.One-sample t-test and two-sample t-test were used for statistical analysis. Results： Compared with thecontrols, the VCIND group exhibited increased functional activity in such DMN regions as the left inferior temporal gyrus, parahippocampal gyrus, and medial frontal gyrus. The VCIND group had decreased functional connectivity of DMN at right superior frontal gyrus, left mid-cingu-late area, the medial part of left superior frontal gyrus, and bilateral medial frontal gyrus. The VCIND group also showed decreased functional connectivity of ECN pri-marilyat left inferior parietal gyrus, right angular gyrus, right middle occipital gyrus, and right middle frontal cor-tex. Conclusions： Increased functional connectivity with-in DMN and decreased functional connectivity within ECN suggested dysfunction of these two networks, which mightbe associated with the cognitive defcitsin patients with VCIND. These fndingsmay help usunderstandthe pathogenesis and clinical characteristics of VCIND.

Developing Environment of Hierarchical and Distributed Real-Time Fire-Control Network

This paper introduces a hierarchical real-time environment for developing ship-bornefire-control system. Advanced computer networks are used to simulate the system with the requiredengagement scenario, including own-ship and parameters, and data processing and transmission,mission calculation, graphical supervision and gunnery ballistics outputting. The simulation systemis able to receive instruction from, or send information to the command-control center. Furthermore,the system can also be used to compare various designed schemes and analyze the accuracy andeffectiveness of the system.

Hierarchical hybrid control network design based on LON and master-slave RS-422/485 protocol

Aiming at the weaknesses of LON bus, combining the coexistence of fieldbus and DCS (Distribu ted Control Systems) in control networks, the authors introduce a hierarchical hybrid control network design based on LON and master slave RS 422/485 protocol. This design adopts LON as the trunk, master slave RS 422/485 control networks are connected to LON as special subnets by dedicated gateways. It is an implementation method for isomerous control network integration. Data management is ranked according to real time requirements for different network data. The core components, such as control network nodes, router and gateway, are detailed in the paper. The design utilizes both communication advantage of LonWorks technology and the more powerful control ability of universal MCUs or PLCs, thus it greatly increases system response speed and performance cost ratio.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

INFORMATION DESIGN OF UNIVERSAL ADJUSTMENT PROGRAM OF PLANE CONTROL NETWORKS

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5-29 Establishment of HIRFL Control Network Monitoring Platform

There are a lot of equipments in HIRFL such as faraday cups, view screens power supplies, RF (radio frequency)equipment, vacuum equipment, and so on. A growing number of equipment support network interface in theseyears. Thus the control network system of HIRFL has become more and more big and complicated. So it is veryimportant to ensure that each device is online. We built a network monitoring platform. There are three functions.The first is monitoring the online or offline state of each switch. Second, the devices in the control network belongto different subsystem and are managed by subsystem managers. We cannot guarantee that each device is notdropped, but we can have a warning system which can warn device manager once the device dropped. Third, ifsome ports are abnormal, the information can be immediately reported to the administrator. Therefore, the controlnetwork monitoring platform has three subsystems, switch monitoring system, equipment offline warning systemand switch log analysis system, as shown in Fig. 1.