Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

DTAIS:Distributed trusted active identity resolution systems for the Industrial Internet

In recent years,the Industrial Internet and Industry 4.0 came into being.With the development of modern industrial intelligent manufacturing technology,digital twins,Web3 and many other digital entity applications are also proposed.These applications apply architectures such as distributed learning,resource sharing,and arithmetic trading,which make high demands on identity authentication,asset authentication,resource addressing,and service location.Therefore,an efficient,secure,and trustworthy Industrial Internet identity resolution system is needed.However,most of the traditional identity resolution systems follow DNS architecture or tree structure,which has the risk of a single point of failure and DDoS attack.And they cannot guarantee the security and privacy of digital identity,personal assets,and device information.So we consider a decentralized approach for identity management,identity authentication,and asset verification.In this paper,we propose a distributed trusted active identity resolution system based on the inter-planetary file system(IPFS)and non-fungible token(NFT),which can provide distributed identity resolution services.And we have designed the system architecture,identity service process,load balancing strategy and smart contract service.In addition,we use Jmeter to verify the performance of the system,and the results show that the system has good high concurrent performance and robustness.

A general Boolean semantic modelling approach for complex and intelligent industrial systems in the framework of DES

Discrete event system(DES)models promote system engineering,including system design,verification,and assessment.The advancement in manufacturing technology has endowed us to fabricate complex industrial systems.Consequently,the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative.Moreover,industrial systems are no longer quiescent,thus the intelligent operations of the systems should be dynamically specified in the model.In this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the model.In traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-consuming and error-prone process.Compared with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continuously adding a new semantic.Thus,this general modeling approach has wide potential for future complex and intelligent industrial systems.

New Industrialization: Characteristics, System Development and Implementation Pathway

New industrialization in China, different from its past economic development pattern or patterns in developed nations, is the country’s theoretical innovation based on the positive and negative experiences of industrialization at home and worldwide. New industrialization has various novel characteristics, including new sources of efficiency, new factors of production, new organizational forms, and new constraints. In addition, it has certain particularities arising from modernization with Chinese characteristics. This article summarizes the characteristics of new industrialization from the perspectives of people-centered approach, quality-first concept, independent innovation, green low-carbon economics, digital-real integration, and open circulation. There are four systems for promoting new industrialization: A self-sustained scientific and technological system, a high-end advanced manufacturing system, a green low-carbon circular system, and a division of labor system with domestic and international circulation. The Chinese new industrialization proposes the pathway and policy measures considering the new global situation and the requirements of new goals of strengthening organization and leadership, reducing factor cost, accelerating independent technological innovation, smoothing domestic and international circulation, and optimizing competition environment.

World Industrial Revolutions and the Development of Artificial Intelligence System

The objective-scientific conclusions obtained from the researches conducted in various fields of science prove that era and worldview are in unity and are phenomena that determine one another,and era and worldview are the most important phenomena in the understanding of geniuses,historical events,including personalities who have left a mark on the history of politics,and every individual as a whole.And it is appropriate to briefly consider the problem in the context of human and personality factors.It is known that man has tried to understand natural phenomena since the beginning of time.Contact with the material world naturally affects his consciousness and even his subconscious as he solves problems that are important or useful for human life.During this understanding,the worldview changes and is formed.Thus,depending on the material and moral development of all spheres of life,the content and essence of the progress events,as the civilizations replaced each other in different periods,the event of periodization took place and became a system.If we take Europe,the people of the Ice Age of 300,000 years ago,who engaged in hunting to solve their hunger needs,in other words,the age of dinosaurs,have spread to many parts of the world from Africa,where they lived in order to survive and meet more of their daily needs.The extensive integration of agricultural Ice Age People into the Earth included farming,fishing,animal husbandry,hunting,as well as handicrafts,etc.,and has led to the revolutionary development of the fields.As economic activities led these first inhabitants of the planet from caves to less comfortable shelters,then to good houses,then to palaces,labor activities in various occupations,including crafts,developed rapidly.Thus,the fads of the era who differed from the crowd(later this class will be called personalities,geniuses...-Kh.G.)began to appear.If we approach the issue from the point of view of history,we witness that the world view determines the development in different periods.This idea can be expressed in such a way that each period can be considered to have developed or experienced a crisis according to the level of worldview.In this direction of our thoughts,the question arises:So,what is the phenomenon of worldview of this era-XXI century?Based on the general content of the current events,characterized as the globalization stage of the modern world,we can say that the outlook of the historical stage we live in is based on the achievements of the last stage of the industrial revolution.In this article,by analyzing the history of the artificial intelligence system during the world industrial revolutions,we will study both the concept of progress of the industrial revolutions and the progressive and at the same time regressive development of the artificial intelligence system.

Discussion and Application of Planning EIA Index System in the Industrial Development Region Based on Low Carbon Economy

Aimed at the low carbon economy and the characteristics of planning EIA in the industrial development region,the planning EIA index system which merged with the low carbon economy concept in the industrial development region was established.The construction planning of national eco-industrial park in Hefei High-tech Industrial Development Zone was comprehensively evaluated by using the gray correlation method.The evaluation results of considering and without considering the low carbon index were contrasted and were verified by using the improved entropy method.The results showed that the regional sustainable development index constantly rose,and the planning was reasonable.Moreover,the suggestions were also put forward to tackle the problems.

NEW MODEL OF THEODOLITE INDUSTRIAL MEASURING SYSTEM

Comparing with the coordinates measuring machine (CMM),the theodolite industrial measuring system (TIMS) can be easily moved and it can measure large sized industrial targets contactlessly.But up to now the precision of the TIMS has been considered so low that the TIMS isnt applied to some precise measurements.The error in self locating TIMS is a main factor which affects the precision of the TIMS.A new model of the TIMS is given out in this paper,and it can eliminate the error in self locating the TIMS.The new model is not only investigated and analyzed theoretically but also verified by the real measured data.

Effects of Urbanization and Industrialization on Farmland System in Shandong Peninsula

[Objective] The aim was to study the effects of urbanization and industrialization on farmland system in Shandong Peninsula. [Method] In Shandong Peninsula, the effects were studied and analyzed using remote sensing and image interpretation with spatial data analysis and statistic data analysis. [Result] During researching periods in Shandong Peninsula, wasteland area changed from decreasing to increasing; farmland area was declining; orchard and forestry areas were increasing, mainly resulting from political policies and benefits maximization of farmers. Meanwhile, chemical fertilizers and agricultural mechanization are more frequently applied with industrialization and urbanization, leading significant effects on environment, industry and urban. [Conclusion] More policies should be formulated to promote harmonious development of society, economy and environment.

Synthesis and application of an environmentally friendly antiscalant in industrial cooling systems

Allylpolyethoxy carboxylate macromonomers possessing polyethylene oxygen long chains were synthesized by advanced technology of the polyether cap.A novel double-hydrophilic block copolymer was prepared through free radical polymeric reactions in aqueous solution and its performance on CaCO3 inhibition and dispersancy activity towards Fe2 O3 was evaluated in recirculating cooling water systems.The study shows that acrylic acid-allylpolyethoxy carboxylate has a significant ability to inhibit the precipitation of calcium carbonate and an excellent dispersing capability to stabilize iron Ⅲ in industrial cooling systems. X-ray diffraction shows that there is a number of vaterite crystals in the presence of the phosphorous free and non-nitrogen copolymer. The change in crystal forms is also confirmed by the Fourier-transform infrared spectra the scanning electron microscopy and the transmission electron microscopy. The inhibition mechanism is proposed and it shows that the interactions between calcium and polyethylene glycol PEG are the fundamental impetus for restraining the formation of the scale in cooling water systems.

Risk Assessment Index System of Natural Gas Industrial Chain in China

This paper establishes a risk assessment index system for the natural gas industrial chain. China＇s natural gas industrial chain is entering a stage of rapid growth. In order to guarantee healthy development of the natural gas industrial chain, it is urgent to establish a risk alert system, which is based on a risk assessment index system. First of all, the risks of the natural gas industrial chain are defined in the paper; then the risk factors are analyzed according to the present status of the natural gas industrial chain, and five categories of risk factors are summarized： resource risk, transport risk, marketing risk, risk of unbalanced chain links, and environment risk. The paper presents the principles of the risk assessment index system. The natural gas industrial chain risk assessment index system is established with four levels and forty-six risk indices.

Application of Serial Port in Industrial Computer System

In an industrial computer control system, if a communication with a single chip computer built with the aid of serial port I/O ports can be got easily to connect with equipment. Meanwhile, the system reliability would be improved and the system cost would be reduced.To save ±15 V power supply and electrical level convert circuit,the author provide a simple interface circuit and operational procedure between PC and the single chip computer in this paper. The application of multithread is introduced and the advantages of data acquisition and processing with multithread are also analyzed.

Data-Driven Based Fault Prognosis for Industrial Systems:A Concise Overview

Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the results of fault prognosis,the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance.With the increased complexity and the improved automation level of industrial systems,fault prognosis techniques have become more and more indispensable.Particularly,the datadriven based prognosis approaches,which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data,gain great attention from different industrial sectors.In this context,the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems.Firstly,the characteristics of different prognosis methods are revealed with the data-based ones being highlighted.Moreover,based on the different data characteristics that exist in industrial systems,the corresponding fault prognosis methodologies are illustrated,with emphasis on analyses and comparisons of different prognosis methods.Finally,we reveal the current research trends and look forward to the future challenges in this field.This review is expected to serve as a tutorial and source of references for fault prognosis researchers.

Fog-IBDIS:Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems

Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy.Second,the function of Fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of Fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data.

MEC Enabled Cooperative Sensing and Resource Allocation for Industrial IoT Systems

In industrial Internet of Things systems,state estimation plays an important role in multisensor cooperative sensing.However,the state information received by remote control center experiences random delay,which inevitably affects the state estimation performance.Moreover,the computation and storage burden of remote control center is very huge,due to the large amount of state information from all sensors.To address this issue,we propose a layered network architecture and design the mobile edge computing(MEC)enabled cooperative sensing scheme.In particular,we first characterize the impact of random delay on the error of state estimation.Based on this,the cooperative sensing and resource allocation are optimized to minimize the state estimation error.The formulated constrained minimization problem is a mixed integer programming problem,which is effectively solved with problem decomposition based on the information content of delivered data packets.The improved marine predators algorithm(MPA)is designed to choose the best edge estimator for each sensor to pretreat the sensory information.Finally,the simulation results show the advantage and effectiveness of proposed scheme in terms of estimation accuracy.

An automatic loading system for rock core testing with an industrial CT scanner

A new type of a loading and measuring system was developed for testing failure and deformation of rock core samples with an industrial CT （ICT） scanner.The loading and measuring system consisted of a loading system and a computer control system.The maximum servo-controlled force was 2 tonnes.The new system was a high-stiffness system with a small size.During ICT tests,rock core samples could be easily loaded in the axial direction.So the initiation,propagation,and coalescence of cracks in core samples were observed on ICT images.

Event-Based Anomaly Detection for Non-Public Industrial Communication Protocols in SDN-Based Control Systems

As the main communication mediums in industrial control networks,industrial communication protocols are always vulnerable to extreme exploitations,and it is very difficult to take protective measures due to their serious privacy.Based on the SDN(Software Defined Network)technology,this paper proposes a novel event-based anomaly detection approach to identify misbehaviors using non-public industrial communication protocols,and this approach can be installed in SDN switches as a security software appliance in SDN-based control systems.Furthermore,aiming at the unknown protocol specification and message format,this approach first restructures the industrial communication sessions and merges the payloads from industrial communication packets.After that,the feature selection and event sequence extraction can be carried out by using the N-gram model and K-means algorithm.Based on the obtained event sequences,this approach finally trains an event-based HMM(Hidden Markov Model)to identify aberrant industrial communication behaviors.Experimental results clearly show that the proposed approach has obvious advantages of classification accuracy and detection efficiency.

Effects of Industrial Relocation on Chinese Regional Economic Growth Disparities: Based on System Dynamics Modeling

The economic growth of China has led to increasing growth disparities between regions. Such disparities are uncontrolled and are severely negative symptoms in the process of economic development. On the basis of system dynamics(SD) modeling and the relationship between industrial relocation and regional economic growth, we construct a model of the interrelationship between the two aforementioned phenomena. The model is an effective and creative exploration for examining effects of industrial relocation on Chinese regional economic growth disparities. The SD model is employed in this study to build an inter-regional labor migration SD model, an inter-regional capital migration SD model, an intra-industry SD model, an intra-regional population SD model, and an intra-regional SD model which are based on realities in labor and capital flow from the view of industrial relocation. VENSIM software is utilized to perform a system simulation based on the data of the eastern, middle, and western regions from 2000 to 2010. Results show that industrial relocation gradually narrows the relative disparity in GDP among the three regions. Moreover, the absolute one is enlarged continuously. The absolute and relative disparities in per capita GDP among eastern, middle, and western regions generally exhibit decreasing trends.

Comparative study on pressure swing adsorption system for industrial hydrogen and fuel cell hydrogen

In order to improve the design of PSA system for fuel cell hydrogen production,a non-isothermal model of eight-bed PSA hydrogen process with five-component(H_(2)/N_(2)/CH_(4)/CO/CO_(2)=74.59%/0.01%/4.2%/2.5%/18.7%(vol))four-stage pressure equalization was developed in this article.The model adopts a composite adsorption bed of activated carbon and zeolite 5 A.In this article,pressure variation,temperature field and separation performance are stimulated,and also effect of providing purge(PP)differential pressure and the ratio of activated carbon to zeolite 5 A on separation performance in the process of producing industrial hydrogen(CO content in hydrogen is 10μl·L^(-1))and fuel cell hydrogen(CO content is 0.2μl·L^(-1))are compared.The results show that Run 3,when the CO content in hydrogen is 10μl·L^(-1),the hydrogen recovery is 89.8%,and the average flow rate of feed gas is 0.529 mol·s^(-1);When the CO content in hydrogen is 0.2μl·L^(-1),the hydrogen recovery is 85.2%,and the average flow rate of feed gas is 0.43 mol·s^(-1).With the increase of PP differential pressure,hydrogen recovery first increases and then decreases,reaching the maximum when PP differential pressure is 0.263 MPa;With the decrease of the ratio of activated carbon to zeolite 5 A,the hydrogen recovery increases gradually.When the CO content in hydrogen is 0.2μl·L^(-1) the hydrogen recovery increases more obviously,from 83.96%to 86.37%,until the ratio of activated carbon to zeolite 5 A decreases to 1.At the end of PP step,no large amount of CO_(2) in gas or solid phase enters the zeolite 5 A adsorption bed,while when the CO content in hydrogen is 10μl·L^(-1),and the ratio of carbon to zeolite 5 A is less than 1.4,more CO_(2) will enter the zeolite 5 A bed.

Smart contract token-based privacy-preserving access control system for industrial Internet of Things

Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily lives.While IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device communications.Hence,at present,a reliable and dynamic access control management system for IIoT is in urgent need.Up till now,numerous access control architectures have been proposed for IIoT.However,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be met.In this paper,we offer a smart contract token-based solution for decentralized access control in IIoT systems.Specifically,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT environments.We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration process.Subsequently,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local blockchain.Finally,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the process.The proliferation of industrial sensors and the availability of thickening-system data make this possible.However,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive models.To address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening systems.Using a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results.The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories.The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.