Adaptive Network Sustainability and Defense Based on Artificial Bees Colony Optimization Algorithm for Nature Inspired Cyber Security

Cyber Defense is becoming a major issue for every organization to keep business continuity intact.The presented paper explores the effectiveness of a meta-heuristic optimization algorithm-Artificial Bees Colony Algorithm(ABC)as an Nature Inspired Cyber Security mechanism to achieve adaptive defense.It experiments on the Denial-Of-Service attack scenarios which involves limiting the traffic flow for each node.Businesses today have adapted their service distribution models to include the use of the Internet,allowing them to effectively manage and interact with their customer data.This shift has created an increased reliance on online services to store vast amounts of confidential customer data,meaning any disruption or outage of these services could be disastrous for the business,leaving them without the knowledge to serve their customers.Adversaries can exploit such an event to gain unauthorized access to the confidential data of the customers.The proposed algorithm utilizes an Adaptive Defense approach to continuously select nodes that could present characteristics of a probable malicious entity.For any changes in network parameters,the cluster of nodes is selected in the prepared solution set as a probable malicious node and the traffic rate with the ratio of packet delivery is managed with respect to the properties of normal nodes to deliver a disaster recovery plan for potential businesses.

Digital Twins and Cyber Physical Systems toward Smart Manufacturing and Industry 4.0:Correlation and Comparison

State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important prerequisite for smart manufacturing is cyber-physical integration,which is increasingly being embraced by manufacturers.As the preferred means of such integration,cyber-physical systems(CPS)and digital twins(DTs)have gained extensive attention from researchers and practitioners in industry.With feedback loops in which physical processes affect cyber parts and vice versa,CPS and DTs can endow manufacturing systems with greater efficiency,resilience,and intelligence.CPS and DTs share the same essential concepts of an intensive cyber-physical connection,real-time interaction,organization integration,and in-depth collaboration.However,CPS and DTs are not identical from many perspectives,including their origin,development,engineering practices,cyber-physical mapping,and core elements.In order to highlight the differences and correlation between them,this paper reviews and analyzes CPS and DTs from multiple perspectives.

A Survey of Cyber Attacks on Cyber Physical Systems:Recent Advances and Challenges

A cyber physical system(CPS)is a complex system that integrates sensing,computation,control and networking into physical processes and objects over Internet.It plays a key role in modern industry since it connects physical and cyber worlds.In order to meet ever-changing industrial requirements,its structures and functions are constantly improved.Meanwhile,new security issues have arisen.A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems,and thus has gained increasing attention from researchers and practitioners.This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems.First,as typical system models are employed to study these systems,time-driven and event-driven systems are reviewed.Then,recent advances on three types of attacks,i.e.,those on availability,integrity,and confidentiality are discussed.In particular,the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders.Namely,both attack and defense strategies are discussed based on different system models.Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

Influential Node Ranking and Invulnerability of Air Traffic Cyber Physical System

To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical coupling relationship between routes and sectors is analyzed,an air traffic CPS network model is constructed,and the indicators of node influence and invulnerability are established.The K-shell algorithm is improved to identify node influence,and the invulnerability is analyzed under random and selective attacks.Taking Airspace in Eastern China as an example,its influential nodes are sorted by degree,namely,K-shell,the improved K-shell(IKS)and betweenness centrality.The invulnerability of air traffic CPS under different attacks is analyzed.Results show that IKS can effectively identify the influential nodes in the air traffic CPS network,and IKS and betweenness centrality are the two key indicators that affect the invulnerability of air traffic CPS.

Prediction-based Manufacturing Center Self-adaptive Demand Side Energy Optimization in Cyber Physical Systems

Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

Semantic Social Service Organization Mechanism in Cyber Physical System

Cyber physical system(CPS)provides more powerful service by cyber and physical features through the wireless communication.As a kind of social organized network system,a fundamental question of CPS is to achieve service self-organization with its nodes autonomously working in both physical and cyber environments.To solve the problem,the social nature of nodes in CPS is firstly addressed,and then a formal social semantic descriptions is presented for physical environment,node service and task in order to make the nodes communicate automatically and physical environment sensibly.Further,the Horn clause is introduced to represent the reasoning rules of service organizing.Based on the match function,which is defined for measurement between semantics,the semantic aware measurement is presented to evaluate whether environment around a node can satisfy the task requirement or not.Moreover,the service capacity evaluation method for nodes is addressed to find out the competent service from both cyber and physical features of nodes.According to aforementioned two measurements,the task semantic decomposition algorithm and the organizing matrix are defined and the service self-organizing mechanism for CPS is proposed.Finally,examinations are given to further verify the efficiency and feasibility of the proposed mechanism.

Finite-time fault-tolerant control of teleoperating cyber physical system against faults

This paper studies a finite-time adaptive fractionalorder fault-tolerant control(FTC)scheme for the slave position tracking of the teleoperating cyber physical system(TCPS)with external disturbances and actuator faults.Based on the fractional Lyapunov stability theory and the finite-time stability theory,a fractional-order nonsingular fast terminal sliding mode(FONFTSM)control law is proposed to promote the tracking and fault tolerance performance of the considered system.Meanwhile,the adaptive fractional-order update laws are designed to cope with the unknown upper bounds of the unknown actuator faults and external disturbances.Furthermore,the finite-time stability of the closed-loop system is proved.Finally,comparison simulation results are also provided to show the validity and the advantages of the proposed techniques.

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical components.The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of CPES.Since security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection.The EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion detection.Besides,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)model.The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects.The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

Chaotic Sandpiper Optimization Based Virtual Machine Scheduling for Cyber-Physical Systems

Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CPS offers huge processing and storage resources for CPS thatfinds helpful for a range of application areas.At the same time,with the massive development of applica-tions that exist in the CPS environment,the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effective-ness of the CC platform,virtualization technologies have been employed for resource management and the applications are executed via virtual machines(VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS,this paper focuses on the design of chaotic sandpi-per optimization based VM scheduling(CSPO-VMS)technique for energy effi-cient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory,which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm,the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives afitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique,a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.

The Characteristic Analyzation of Cyber Physical Systems

Cyber-physical systems （CPS） are complex distributed heterogeneous systems which integrating cyber and physical processes by computation, communication and control. During interaction between cyber and physical world, the traditional theories and applications has been difficult to satisfy real-time performance and efficient. Cyber-physical systems clearly have a role to play in developing a new theory of computer-mediated physical systems. The aim of this work is to analysis the features and relation technology of CPS that get better understanding for this new field. We summarized the research progresses from different perspectives such as modeling, classical tools and applications. Finally, the research challenges for CPS are in brief outlined.

Hybrid Flow Model of Cyber Physical Distribution Network and an Instantiated Decentralized Control Application

With the access to large amounts of renewable energy sources(RES),operation uncertainty of distribution networks increases significantly.Fortunately,adopting advanced information and communication technology,a cyber-physical distribution network(CPDS)provides the possibility to solve this problem via aggregative management of decentralized controllable loads.However,information flow in cyber space deeply interacts with energy flow in physical space,leading to a complexity in modeling,design and analysis of the whole control process.To deal with this problem,a general hybrid flow model of CPDS is first proposed in this paper.In this model,the control process is abstracted into interactions among three types of cyber nodes through cyber branches.The mathematic model of cyber nodes and branches is developed as well as that of the controlled physical object for hybrid flow computation.Then,based on the hybrid model,an instantiated application to compensate feeder power deviation caused by RES fluctuation through aggregative control of large-scale air-conditioners(ACs)is investigated.In this application,coordinative control of the AC cluster is achieved through a decentralized control strategy with very little communication cost and very good privacy protection.Results of numerical examples verify the correctness and flexibility of the hybrid flow model in reflecting interactions between cyber flow and energy flow as well as system operations.The proposed decentralized control strategy of the AC cluster is also proven to be effective and robust in FCE compensation.

AI-Based Modeling and Data-Driven Evaluation for Smart Manufacturing Processes

Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things(IIOT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management.Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart.We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.

Smart Manufacturing and Intelligent Manufacturing:A Comparative Review

The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world.However,different terminologies,namely smart manufacturing(SM)and intelligent manufacturing(IM),have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners.While SM and IM are similar,they are not identical.From an evolutionary perspective,there has been little consideration on whether the definition,thought,connotation,and technical development of the concepts of SM or IM are consistent in the literature.To address this gap,the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM.A bibliometric analysis of publication sources,annual publication numbers,keyword frequency,and top regions of research and development establishes the scope and trends of the currently presented research.Critical topics discussed include origin,definitions,evolutionary path,and key technologies of SM and IM.The implementation architecture,standards,and national focus are also discussed.In this work,a basis to understand SM and IM is provided,which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.

A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

Communication Scheduling and Remote Estimation With Adversarial Intervention

We study a communication scheduling and remote estimation problem within a worst-case scenario that involves a strategic adversary. Specially, a remote sensing system consisting of a sensor, an encoder and a decoder is configured to observe,transmit, and recover a discrete time stochastic process. At each time step, the sensor makes an observation on the state variable of the stochastic process. The sensor is constrained by the number of transmissions over the time horizon, and thus it needs to decide whether to transmit its observation or not after making each measurement. If the sensor decides to transmit,it sends the observation to the encoder, who then encodes and transmits the observation to the decoder. Otherwise, the sensor and the encoder maintain silence. The decoder is required to generate a real-time estimate on the state variable. The sensor,the encoder, and the decoder collaborate to minimize the sum of the communication cost for the sensor, the encoding cost for the encoder, and the estimation error for the decoder. There is also a jammer interfering with the communication between the encoder and the decoder, by injecting an additive channel noise to the communication channel. The jammer is charged for the jamming power and is rewarded for the estimation error generated by the decoder, and it aims to minimize its net cost. We consider a feedback Stackelberg game with the sensor, the encoder, and the decoder as the composite leader, and the jammer as the follower. Under some technical assumptions, we obtain a feedback Stackelberg solution, which is threshold based for the scheduler,and piecewise affine for the encoder and the decoder. We also generate numerical results to demonstrate the performance of the remote sensing system under the feedback Stackelberg solution.

Toward Energy-Efficient and Trustworthy eHealth Monitoring System

The rapid technological convergence between Internet of Things （loT）, Wireless Body Area Networks （WBANs） and cloud computing has made e-healthcare emerge as a promising application domain, which has significant potential to improve the quality of medical care. In particular, patient-centric health monitoring plays a vital role in e-healthcare service, involving a set of important operations ranging from medical data collection and aggregation, data transmission and segregation, to data analytics. This survey paper firstly presents an architectural framework to describe the entire monitoring life cycle and highlight the essential service components. More detailed discussions are then devoted to {／em data collection} at patient side, which we argue that it serves as fundamental basis in achieving robust, efficient, and secure health monitoring. Subsequently, a profound discussion of the security threats targeting eHealth monitoring systems is presented, and the major limitations of the existing solutions are analyzed and extensively discussed. Finally, a set of design challenges is identified in order to achieve high quality and secure patient-centric monitoring schemes, along with some potential solutions.

Deep Learning with Image Classification Based Secure CPS for Healthcare Sector

Cyber-Physical System(CPS)involves the combination of physical processes with computation and communication systems.The recent advancementsmade in cloud computing,Wireless Sensor Network(WSN),healthcare sensors,etc.tend to develop CPS as a proficient model for healthcare applications especially,home patient care.Though several techniques have been proposed earlier related to CPS structures,only a handful of studies has focused on the design of CPS models for health care sector.So,the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy.To overcome the challenges,the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS(DLIDIC-SCPS)model for healthcare sector.The aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare sector.Primarily,data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network(FDNN)technique.Besides,Multiple Share Creation(MSC)approach is used to create several shares of medical image so as to accomplish security.Also,blockchain is employed as a distributed data storage entity to create a ledger that provides access to the client.For image classification,Inception v3 with Fuzzy Wavelet Neural Network(FWNN)is utilized that diagnose the disease from the applied medical image.Finally,Salp Swarm Algorithm(SSA)is utilized to fine tune the parameters involved in WNN model,thereby boosting its classification performance.A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS technique.The simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security,privacy,and image classification outcomes over recent state-of-the-art techniques.

Threshold-Based Adaptive Gaussian Mixture Model Integration(TA-GMMI)Algorithm for Mapping Snow Cover in Mountainous Terrain

Snow cover is an important parameter in the fields of computer modeling,engineering technology and energy development.With the extensive growth of novel hardware and software compositions creating smart,cyber physical systems’(CPS)efficient end-to-end workflows.In order to provide accurate snow detection results for the CPS’s terminal,this paper proposed a snow cover detection algorithm based on the unsupervised Gaussian mixture model(GMM)for the FY-4A satellite data.At present,most snow cover detection algorithms mainly utilize the characteristics of the optical spectrum,which is based on the normalized difference snow index(NDSI)with thresholds in different wavebands.These algorithms require a large amount of manually labeled data for statistical analysis to obtain the appropriate thresholds for the study area.Consideration must be given to both the high and low elevations in the study area.It is difficult to extract all snow by a fixed threshold in mountainous and rugged terrains.In this research,we avoid relying on a manual analysis for different elevations.Therefore,an algorithm based on the GMM is proposed,integrating the threshold-based algorithm and the GMM.First,the threshold-based algorithm with transferred thresholds from other satellites’analysis results are used to coarsely classify the surface objects.These results are then used to initialize the parameters of the GMM.Finally,the parameters of that model are updated by an expectation-maximum(EM)iteration algorithm,and the final results are outputted when the iterative conditions end.The results show that this algorithm can adjust itself to mountainous terrain with different elevations,and exhibits a better performance than the threshold-based algorithm.Compared with orbit satellites’snow products,the accuracy of the algorithm used for FY-4A is improved by nearly 2%,and the snow detection rate is increased by nearly 6%.Moreover,compared with microwave sensors’snow products,the accuracy is increased by nearly 3%.The validation results show that the proposed algorithm can be adapted to a complex terrain environment in mountainous areas and exhibits good performance under a transferred threshold without manually assigned labels.

Model Free Adaptive Predictive Control of Desulfurization Slurry pH Based on CPS Framework

In order to improve the slurry pH control accuracy of the absorption tower in the wet flue gas desulfurization process,a model free adaptive predictive control algorithm for the desulfurization slurry pH which is based on a cyber physical systems framework is proposed.First,aiming to address system characteristics of non-linearity and pure hysteresis in slurry pH change process,a model free adaptive predictive control algorithm based on compact form dynamic linearization is proposed by combining model free adaptive control algorithm with model predictive control algorithm.Then,by integrating information resources with the physical resources in the absorption tower slurry pH control process,an absorption tower slurry pH optimization control system based on cyber physical systems is constructed.It is turned out that the model free adaptive predictive control algorithm under the framework of the cyber physical systems can effectively realize the high-precision tracking control of the slurry pH of the absorption tower,and it has strong robustness.