With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Further...With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.展开更多
The concept of Cyber-Physical Systems(CPS)enables the creation of a complex network that includes sensors integrated into vehicles and infrastructure,facilitating seamless data acquisition and transfer.This review exa...The concept of Cyber-Physical Systems(CPS)enables the creation of a complex network that includes sensors integrated into vehicles and infrastructure,facilitating seamless data acquisition and transfer.This review examines the convergence of CPS and Industry 4.0 in the smart transportation sector,highlighting their transformative impact on Intelligent Transportation Systems(ITS)operations.It explores the integration of Industry 4.0 and CPS technologies in intelligent transportation,highlighting their roles in enhancing efficiency,safety,and sustainability.A systematic framework is proposed for developing,implementing,and managing these technologies in the transportation industry.Moreover,the review discusses frequent obstacles during technology integration in transportation and presents future research trends and innovations in intelligent transportation operations post-Industry 4.0 and CPS integration.Lastly,it emphasizes the critical need for standardized protocols and encryption methodologies to enhance the security of communication and data exchange among CPS components in transportation infrastructure.展开更多
Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(I...Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(ITS).Integrating CPS-ITS and IoT provides real-time Vehicle-to-Infrastructure(V2I)communication,supporting better traffic management,safety,and efficiency.These technological innovations generate complex problems that need to be addressed,uniquely about data routing and Task Scheduling(TS)in ITS.Attempts to solve those problems were primarily based on traditional and experimental methods,and the solutions were not so successful due to the dynamic nature of ITS.This is where the scope of Machine learning(ML)and Swarm Intelligence(SI)has significantly impacted dealing with these challenges;in this line,this research paper presents a novel method for TS and data routing in the CPS-ITS.This paper proposes using a cutting-edge ML algorithm for data transmission from CPS-ITS.This ML has Gated Linear Unit-approximated Reinforcement Learning(GLRL).Greedy Iterative-Particle Swarm Optimization(GI-PSO)has been recommended to develop the Particle Swarm Optimization(PSO)for TS.The primary objective of this study is to enhance the security and effectiveness of ITS systems that utilize CPS-ITS.This study trained and validated the models using a network simulation dataset of 50 nodes from numerous ITS environments.The experiments demonstrate that the proposed GLRL reduces End-toEnd Delay(EED)by 12%,enhances data size use from 83.6%to 88.6%,and achieves higher bandwidth allocation,particularly in high-demand scenarios such as multimedia data streams where adherence improved to 98.15%.Furthermore,the GLRL reduced Network Congestion(NC)by 5.5%,demonstrating its efficiency in managing complex traffic conditions across several environments.The model passed simulation tests in three different environments:urban(UE),suburban(SE),and rural(RE).It met the high bandwidth requirements,made task scheduling more efficient,and increased network throughput(NT).This proved that it was robust and flexible enough for scalable ITS applications.These innovations provide robust,scalable solutions for real-time traffic management,ultimately improving safety,reducing NC,and increasing overall NT.This study can affect ITS by developing it to be more responsive,safe,and effective and by creating a perfect method to set up UE,SE,and RE.展开更多
This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the ad...This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the adverse effects of quantization errors on system performance,a coupling sliding mode surface is established for each following vehicle.The radial basis function(RBF) neural networks are employed to approximate the unknown external disturbances.Then,a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles.And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle's controller parameters is independent of the entire communication topology.The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero.Compared with the existing controller design methods and quantization mechanisms,the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors,driving comfort and fuel economy.Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.展开更多
This paper designs distributed Nash equilibrium seeking strategies for heterogeneous dynamic cyber-physical systems.In particular, we are concerned with parametric uncertainties in the control channel of the players. ...This paper designs distributed Nash equilibrium seeking strategies for heterogeneous dynamic cyber-physical systems.In particular, we are concerned with parametric uncertainties in the control channel of the players. Moreover, the weights on communication links can be compromised by time-varying uncertainties, which can result from possibly malicious attacks,faults and disturbances. To deal with the unavailability of measurement of optimization errors, an output observer is constructed,based on which adaptive laws are designed to compensate for physical uncertainties. With adaptive laws, a new distributed Nash equilibrium seeking strategy is designed by further integrating consensus protocols and gradient search algorithms.Moreover, to further accommodate compromised communication weights resulting from cyber-uncertainties, the coupling strengths of the consensus module are designed to be adaptive. As a byproduct, the coupling strengths are independent of any global information. With theoretical investigations, it is proven that the proposed strategies are resilient to these uncertainties and players' actions are convergent to the Nash equilibrium. Simulation examples are given to numerically validate the effectiveness of the proposed strategies.展开更多
Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widel...Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widely utilized across various domains,such as smart grids,smart healthcare systems,smart vehicles,and smart manufacturing,among others.Due to their unique spatial distribution,CPSs are highly vulnerable to cyber-attacks,which may result in severe performance degradation and even system instability.Consequently,the security concerns of CPSs have attracted significant attention in recent years.In this paper,a comprehensive survey on the security issues of CPSs under cyber-attacks is provided.Firstly,mathematical descriptions of various types of cyberattacks are introduced in detail.Secondly,two types of secure estimation and control processing schemes,including robust methods and active methods,are reviewed.Thirdly,research findings related to secure control and estimation problems for different types of CPSs are summarized.Finally,the survey is concluded by outlining the challenges and suggesting potential research directions for the future.展开更多
The rapid evolution of smart cities has led to the deployment of Cyber-Physical IoT Systems(CPS-IoT)for real-time monitoring,intelligent decision-making,and efficient resource management,particularly in intelligent tr...The rapid evolution of smart cities has led to the deployment of Cyber-Physical IoT Systems(CPS-IoT)for real-time monitoring,intelligent decision-making,and efficient resource management,particularly in intelligent transportation and vehicular networks.Edge intelligence plays a crucial role in these systems by enabling low-latency processing and localized optimization for dynamic,data-intensive,and vehicular environments.However,challenges such as high computational overhead,uneven load distribution,and inefficient utilization of communication resources significantly hinder scalability and responsiveness.Our research presents a robust framework that integrates artificial intelligence and edge-level traffic prediction for CPS-IoT systems.Distributed computing for selecting forwarders and analyzing threats across the IoT system enhances stability while improving energy efficiency.In addition,to achieve efficient routing decision-making,the Artificial Bee Colony algorithmis explored to enhance the effective utilization of network resources across IoT systems.Based on the simulation results,the proposed framework achieves remarkable performance in terms of throughput by 38%–41%,packet loss ratio by 30%–33%,security risk mitigation by 35%–37%,and trust level by 41%–44%as compared to existing work.展开更多
Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV...Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on CPN.However,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)algorithms.Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy.To address this issue,we propose a social relationship-driven privacy customization protection model for publishers and co-photographers.We construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest similarity.To protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be protected.Our proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.展开更多
While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the se...While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependen-cies between the physical and cyber networks.As a result,the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks.Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems.Firstly,they rely on the assumption of a uniform and strong level of interdependency.That is,once a node within a network fails its counterpart fails immedi-ately.Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure,that now serve as a cyber-physical system.The last is responsible not only for the physical operation(e.g.,flow of vehicles)but also for the continuous data transmission and subsequently the cy-ber operation of the entire transportation network.Therefore,the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects.In this paper,we propose a new robustness assessment approach for modern transportation networks and their underlying in-terdependent physical and cyber network,subjected to earthquake events.The novelty relies on the modelling of interdependent networks,in the form of a graph,based on their interdependency levels.We associate the service-ability level of the coupled physical and cyber network with the damage states induced by earthquake events.Robustness is then measured as a degradation of the cyber-physical serviceability level.The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure.Furthermore,we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level.Results indicate an improvement in robustness level(i.e.,41%)by adopting the proposed robustness improvement indicator.The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdepen-dencies and key node protection strategies.The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.展开更多
This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and aχ^(2) detector.When measurements are transmitted via wireless networks to a remote estimator,th...This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and aχ^(2) detector.When measurements are transmitted via wireless networks to a remote estimator,the innovation sequence becomes susceptible to interception and manipulation by adversaries.We consider a class of linear deception attacks,wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector.Given the inherent volatility of the detection function based on theχ^(2) detector,we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution of the innovation.This broadening enables the design of stealthy attacks that exploit the tolerance inherent in the detection mechanism.The state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrix of the remote estimator under these conditions.The selected degree of deviation is combined with the error covariance to establish the objective function and the attack scheme is acquired by solving an optimization problem.Furthermore,we propose a novel detection algorithm that employs a majority-voting mechanism to determine whether the system is under attack,with decision parameters dynamically adjusted in response to system behavior.This approach enhances sensitivity to stealthy and persistent attacks without increasing the false alarm rate.Simulation results show that the designed leads to about a 41%rise in the trace of error covariance for stable systems and 29%for unstable systems,significantly impairing estimation performance.Concurrently,the proposed detection algorithm enhances the attack detection rate by 33%compared to conventional methods.展开更多
In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are qu...In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.展开更多
Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats.Attackers can non-invasively manipulate sensors and spoof controllers,which in tur...Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats.Attackers can non-invasively manipulate sensors and spoof controllers,which in turn increases the autonomy of the system.Even though the focus on protecting against sensor attacks increases,there is still uncertainty about the optimal timing for attack detection.Existing systems often struggle to manage the trade-off between latency and false alarm rate,leading to inefficiencies in real-time anomaly detection.This paper presents a framework designed to monitor,predict,and control dynamic systems with a particular emphasis on detecting and adapting to changes,including anomalies such as“drift”and“attack”.The proposed algorithm integrates a Transformer-based Attention Generative Adversarial Residual model,which combines the strengths of generative adversarial networks,residual networks,and attention algorithms.The system operates in two phases:offline and online.During the offline phase,the proposed model is trained to learn complex patterns,enabling robust anomaly detection.The online phase applies a trained model,where the drift adapter adjusts the model to handle data changes,and the attack detector identifies deviations by comparing predicted and actual values.Based on the output of the attack detector,the controller makes decisions then the actuator executes suitable actions.Finally,the experimental findings show that the proposed model balances detection accuracy of 99.25%,precision of 98.84%,sensitivity of 99.10%,specificity of 98.81%,and an F1-score of 98.96%,thus provides an effective solution for dynamic and safety-critical environments.展开更多
The growing intersection of gynecological cancer diagnosis and cybersecurity vulnerabilities in healthcare necessitates integrated solutions that address both diagnostic accuracy and data protection.With increasing re...The growing intersection of gynecological cancer diagnosis and cybersecurity vulnerabilities in healthcare necessitates integrated solutions that address both diagnostic accuracy and data protection.With increasing reliance on IoT-enabled medical devices,digital twins,and interconnected healthcare systems,the risk of cyberphysical attacks has escalated significantly.Traditional approaches to machine learning(ML)-based diagnosis often lack real-time threat adaptability and privacy preservation,while cybersecurity frameworks fall short in maintaining clinical relevance.This study introduces HealthSecureNet,a novel Cyber-Integrated Predictive Framework designed to detect gynecological cancer and mitigate cybersecurity threats in real time simultaneously.The proposed model employs a three-tier ML architecture incorporating Gradient Boosting and Support Vector Machines(SVMs)for accurate cancer classification,combined with an adaptive anomaly detection layer leveraging Mahalanobis Distance and severity scoring for threat prioritization.To enhance resilience,the framework integrates Zero Trust principles and Federated Learning(FL),enabling secure,decentralized model training while preserving patient privacy and meeting compliance with HIPAA(Health Insurance Portability and Accountability Act)and GDPR(General Data Protection Regulations).Experimental evaluation using a real-world healthcare cybersecurity dataset demonstrated high accuracy(95.2%),precision(94.3%),recall(91.7%),and AUC-ROC(Area Under the Curve-Receiver Operating Characteristic)(0.94),with a low false positive rate(3.6%).HealthSecureNet outperforms traditional models such as SVM,Random Forest(RF),and k-NN(k-Nearest Neighbor)in both anomaly detection and severity classification accuracy.Its adaptive thresholding and response prioritization mechanisms make it suitable for dynamic healthcare environments,enabling early cancer detection and proactive cyber threatmitigationwithout compromising performance or regulatory standards.This research contributes a robust,dual-purpose solution that enhances both clinical diagnostics and cybersecurity,setting a precedent for future AI(Artificial Intelligence)-driven healthcare systems.展开更多
The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.展开更多
Cyber-physical systems(CPSs)are increasingly vulnerable to cyber-attacks due to their integral connection between cyberspace and the physical world,which is augmented by Internet connectivity.This vulnerability necess...Cyber-physical systems(CPSs)are increasingly vulnerable to cyber-attacks due to their integral connection between cyberspace and the physical world,which is augmented by Internet connectivity.This vulnerability necessitates a heightened focus on developing resilient control mechanisms for CPSs.However,current observer-based active compensation resilient controllers exhibit poor performance against stealthy deception attacks(SDAs)due to the difficulty in accurately reconstructing system states because of the stealthy nature of these attacks.Moreover,some non-active compensation approaches are insufficient when there is a complete loss of actuator control authority.To address these issues,we introduce a novel learning-based passive resilient controller(LPRC).Our approach,unlike observer-based state reconstruction,shows enhanced effectiveness in countering SDAs.We developed a safety state set,represented by an ellipsoid,to ensure CPS stability under SDA conditions,maintaining system trajectories within this set.Additionally,by employing deep reinforcement learning(DRL),the LPRC acquires the capacity to adapt and diverse evolving attack strategies.To empirically substantiate our methodology,various attack methods were compared with current passive and active compensation resilient control methods to evaluate their performance.展开更多
Cyber-physical systems(CPS)represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing,healthcare,and autonomous infrastructure.However,t...Cyber-physical systems(CPS)represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing,healthcare,and autonomous infrastructure.However,their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats,potentially leading to operational failures and data breaches.Furthermore,CPS faces significant threats related to unauthorized access,improper management,and tampering of the content it generates.In this paper,we propose an intrusion detection system(IDS)optimized for CPS environments using a hybrid approach by combining a natureinspired feature selection scheme,such as Grey Wolf Optimization(GWO),in connection with the emerging Light Gradient Boosting Machine(LightGBM)classifier,named as GWO-LightGBM.While gradient boosting methods have been explored in prior IDS research,our novelty lies in proposing a hybrid approach targeting CPS-specific operational constraints,such as low-latency response and accurate detection of rare and critical attack types.We evaluate GWO-LightGBM against GWO-XGBoost,GWO-CatBoost,and an artificial neural network(ANN)baseline using the NSL-KDD and CIC-IDS-2017 benchmark datasets.The proposed models are assessed across multiple metrics,including accuracy,precision,recall,and F1-score,with an emphasis on class-wise performance and training efficiency.The proposed GWO-LightGBM model achieves the highest overall accuracy(99.73%)for NSL-KDD and(99.61%)for CIC-IDS-2017,demonstrating superior performance in detecting minority classes such as Remote-to-Local(R2L)and Other attacks—commonly overlooked by other classifiers.Moreover,the proposed model consumes lower training time,highlighting its practical feasibility and scalability for real-time CPS deployment.展开更多
Abstract Performance optimization of cyber-physical systems （CPS） calls for co-design strategies that handle the issues in both computing domain and physical domain. Periods of controller tasks integrated into a uni...Abstract Performance optimization of cyber-physical systems （CPS） calls for co-design strategies that handle the issues in both computing domain and physical domain. Periods of controller tasks integrated into a uniprocessor system are related to both control performance and real-time schedu- lability analysis simultaneously. System performance improvement can be achieved by optimizing the periods of controller tasks. This paper extends an existing model to select task periods in real-time for CPS with fixed priority controller tasks scheduled by rate-monotonic algorithm. When all the tasks can be integrated, the analytic solution of the problem is derived by using the method of Lagrange multipliers and gradient descent method is evaluated to be suitable online. To further deal with the condition that the system is overloaded, an integrated method is proposed to select periods of tasks online by selecting a subset of tasks first and then optimizing the periods for them. Experimental results demonstrate that our method yields near-optimal result with a short running time.展开更多
With the booming of cyber attacks and cyber criminals against cyber-physical systems(CPSs),detecting these attacks remains challenging.It might be the worst of times,but it might be the best of times because of opport...With the booming of cyber attacks and cyber criminals against cyber-physical systems(CPSs),detecting these attacks remains challenging.It might be the worst of times,but it might be the best of times because of opportunities brought by machine learning(ML),in particular deep learning(DL).In general,DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data.DL models are adopted quickly to cyber attacks against CPS systems.In this survey,a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context.A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems.The methodology includes CPS scenario analysis,cyber attack identification,ML problem formulation,DL model customization,data acquisition for training,and performance evaluation.The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules.Moreover,excellent performance is achieved partly because of several highquality datasets that are readily available for public use.Furthermore,challenges,opportunities,and research trends are pointed out for future research.展开更多
Cyber-physical systems （CPSs） are integrations of computation, communication, control and physical processes. Typical examples where CPSs are deployed include smart grids, civil infrastructure, medical devices and m...Cyber-physical systems （CPSs） are integrations of computation, communication, control and physical processes. Typical examples where CPSs are deployed include smart grids, civil infrastructure, medical devices and manufacturing. Security is one of the most important issues that should be investigated in CPSs and hence has received much attention in recent years. This paper surveys recent results in this area and mainly focusses on three important categories： attack detection, attack design and secure estimation and control. We also discuss several future research directions including risk assessment, modeling of attacks and attacks design, counter-attack strategy and testbed and validation.展开更多
With the concepts of Industry 4.0 and smart manufacturing gaining popularity,there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm,targeting innovation,automation,be...With the concepts of Industry 4.0 and smart manufacturing gaining popularity,there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm,targeting innovation,automation,better response to customer needs,and intelligent systems.Within this context,this review focuses on the concept of cyber–physical production system(CPPS)and presents a holistic perspective on the role of the CPPS in three key and essential drivers of this transformation:data-driven manufacturing,decentralized manufacturing,and integrated blockchains for data security.The paper aims to connect these three aspects of smart manufacturing and proposes that through the application of data-driven modeling,CPPS will aid in transforming manufacturing to become more intuitive and automated.In turn,automated manufacturing will pave the way for the decentralization of manufacturing.Layering blockchain technologies on top of CPPS will ensure the reliability and security of data sharing and integration across decentralized systems.Each of these claims is supported by relevant case studies recently published in the literature and from the industry;a brief on existing challenges and the way forward is also provided.展开更多
基金supported by the National Natural Science Foundation of China under Grant 62471493supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066。
文摘With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.
文摘The concept of Cyber-Physical Systems(CPS)enables the creation of a complex network that includes sensors integrated into vehicles and infrastructure,facilitating seamless data acquisition and transfer.This review examines the convergence of CPS and Industry 4.0 in the smart transportation sector,highlighting their transformative impact on Intelligent Transportation Systems(ITS)operations.It explores the integration of Industry 4.0 and CPS technologies in intelligent transportation,highlighting their roles in enhancing efficiency,safety,and sustainability.A systematic framework is proposed for developing,implementing,and managing these technologies in the transportation industry.Moreover,the review discusses frequent obstacles during technology integration in transportation and presents future research trends and innovations in intelligent transportation operations post-Industry 4.0 and CPS integration.Lastly,it emphasizes the critical need for standardized protocols and encryption methodologies to enhance the security of communication and data exchange among CPS components in transportation infrastructure.
基金funded by Taif University,Taif,Saudi Arabia,project number(TU-DSPP-2024-17)。
文摘Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(ITS).Integrating CPS-ITS and IoT provides real-time Vehicle-to-Infrastructure(V2I)communication,supporting better traffic management,safety,and efficiency.These technological innovations generate complex problems that need to be addressed,uniquely about data routing and Task Scheduling(TS)in ITS.Attempts to solve those problems were primarily based on traditional and experimental methods,and the solutions were not so successful due to the dynamic nature of ITS.This is where the scope of Machine learning(ML)and Swarm Intelligence(SI)has significantly impacted dealing with these challenges;in this line,this research paper presents a novel method for TS and data routing in the CPS-ITS.This paper proposes using a cutting-edge ML algorithm for data transmission from CPS-ITS.This ML has Gated Linear Unit-approximated Reinforcement Learning(GLRL).Greedy Iterative-Particle Swarm Optimization(GI-PSO)has been recommended to develop the Particle Swarm Optimization(PSO)for TS.The primary objective of this study is to enhance the security and effectiveness of ITS systems that utilize CPS-ITS.This study trained and validated the models using a network simulation dataset of 50 nodes from numerous ITS environments.The experiments demonstrate that the proposed GLRL reduces End-toEnd Delay(EED)by 12%,enhances data size use from 83.6%to 88.6%,and achieves higher bandwidth allocation,particularly in high-demand scenarios such as multimedia data streams where adherence improved to 98.15%.Furthermore,the GLRL reduced Network Congestion(NC)by 5.5%,demonstrating its efficiency in managing complex traffic conditions across several environments.The model passed simulation tests in three different environments:urban(UE),suburban(SE),and rural(RE).It met the high bandwidth requirements,made task scheduling more efficient,and increased network throughput(NT).This proved that it was robust and flexible enough for scalable ITS applications.These innovations provide robust,scalable solutions for real-time traffic management,ultimately improving safety,reducing NC,and increasing overall NT.This study can affect ITS by developing it to be more responsive,safe,and effective and by creating a perfect method to set up UE,SE,and RE.
基金supported by the National Natural Science Foundation of China(62173079,62473203)Liaoning Provincial Science and Technology Plan Joint Program(2024-MSLH-019)+1 种基金the Education Department of Liaoning Province(LJKMZ20221840)Interdisciplinary project of Dalian University(DLUXK-2024-YB-004)。
文摘This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the adverse effects of quantization errors on system performance,a coupling sliding mode surface is established for each following vehicle.The radial basis function(RBF) neural networks are employed to approximate the unknown external disturbances.Then,a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles.And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle's controller parameters is independent of the entire communication topology.The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero.Compared with the existing controller design methods and quantization mechanisms,the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors,driving comfort and fuel economy.Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.
基金supported by the National Key R&D Program of China(2022ZD0119604)the National Natural Science Foundation of China(NSFC)(62173181,62222308,62221004)the Natural Science Foundation of Jiangsu Province(BK20220139)
文摘This paper designs distributed Nash equilibrium seeking strategies for heterogeneous dynamic cyber-physical systems.In particular, we are concerned with parametric uncertainties in the control channel of the players. Moreover, the weights on communication links can be compromised by time-varying uncertainties, which can result from possibly malicious attacks,faults and disturbances. To deal with the unavailability of measurement of optimization errors, an output observer is constructed,based on which adaptive laws are designed to compensate for physical uncertainties. With adaptive laws, a new distributed Nash equilibrium seeking strategy is designed by further integrating consensus protocols and gradient search algorithms.Moreover, to further accommodate compromised communication weights resulting from cyber-uncertainties, the coupling strengths of the consensus module are designed to be adaptive. As a byproduct, the coupling strengths are independent of any global information. With theoretical investigations, it is proven that the proposed strategies are resilient to these uncertainties and players' actions are convergent to the Nash equilibrium. Simulation examples are given to numerically validate the effectiveness of the proposed strategies.
文摘Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widely utilized across various domains,such as smart grids,smart healthcare systems,smart vehicles,and smart manufacturing,among others.Due to their unique spatial distribution,CPSs are highly vulnerable to cyber-attacks,which may result in severe performance degradation and even system instability.Consequently,the security concerns of CPSs have attracted significant attention in recent years.In this paper,a comprehensive survey on the security issues of CPSs under cyber-attacks is provided.Firstly,mathematical descriptions of various types of cyberattacks are introduced in detail.Secondly,two types of secure estimation and control processing schemes,including robust methods and active methods,are reviewed.Thirdly,research findings related to secure control and estimation problems for different types of CPSs are summarized.Finally,the survey is concluded by outlining the challenges and suggesting potential research directions for the future.
基金supported and funded by theDeanship of Scientific Research at ImamMohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘The rapid evolution of smart cities has led to the deployment of Cyber-Physical IoT Systems(CPS-IoT)for real-time monitoring,intelligent decision-making,and efficient resource management,particularly in intelligent transportation and vehicular networks.Edge intelligence plays a crucial role in these systems by enabling low-latency processing and localized optimization for dynamic,data-intensive,and vehicular environments.However,challenges such as high computational overhead,uneven load distribution,and inefficient utilization of communication resources significantly hinder scalability and responsiveness.Our research presents a robust framework that integrates artificial intelligence and edge-level traffic prediction for CPS-IoT systems.Distributed computing for selecting forwarders and analyzing threats across the IoT system enhances stability while improving energy efficiency.In addition,to achieve efficient routing decision-making,the Artificial Bee Colony algorithmis explored to enhance the effective utilization of network resources across IoT systems.Based on the simulation results,the proposed framework achieves remarkable performance in terms of throughput by 38%–41%,packet loss ratio by 30%–33%,security risk mitigation by 35%–37%,and trust level by 41%–44%as compared to existing work.
基金supported in part by National Natural Science Foundation of China(62271096,U20A20157)Natural Science Foundation of Chongqing,China(cstc2020jcyj-zdxmX0024,CSTB2022NSCQMSX0600)+5 种基金University Innovation Research Group of Chongqing(CXQT20017)Program for Innovation Team Building at Institutions of Higher Education in Chongqing(CXTDX201601020)Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000626)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN202000626Chongqing Municipal Technology Innovation and Application Development Special Key Project(cstc2020jscx-dxwtBX0053)。
文摘Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on CPN.However,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)algorithms.Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy.To address this issue,we propose a social relationship-driven privacy customization protection model for publishers and co-photographers.We construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest similarity.To protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be protected.Our proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.
文摘While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependen-cies between the physical and cyber networks.As a result,the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks.Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems.Firstly,they rely on the assumption of a uniform and strong level of interdependency.That is,once a node within a network fails its counterpart fails immedi-ately.Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure,that now serve as a cyber-physical system.The last is responsible not only for the physical operation(e.g.,flow of vehicles)but also for the continuous data transmission and subsequently the cy-ber operation of the entire transportation network.Therefore,the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects.In this paper,we propose a new robustness assessment approach for modern transportation networks and their underlying in-terdependent physical and cyber network,subjected to earthquake events.The novelty relies on the modelling of interdependent networks,in the form of a graph,based on their interdependency levels.We associate the service-ability level of the coupled physical and cyber network with the damage states induced by earthquake events.Robustness is then measured as a degradation of the cyber-physical serviceability level.The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure.Furthermore,we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level.Results indicate an improvement in robustness level(i.e.,41%)by adopting the proposed robustness improvement indicator.The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdepen-dencies and key node protection strategies.The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.
文摘This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and aχ^(2) detector.When measurements are transmitted via wireless networks to a remote estimator,the innovation sequence becomes susceptible to interception and manipulation by adversaries.We consider a class of linear deception attacks,wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector.Given the inherent volatility of the detection function based on theχ^(2) detector,we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution of the innovation.This broadening enables the design of stealthy attacks that exploit the tolerance inherent in the detection mechanism.The state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrix of the remote estimator under these conditions.The selected degree of deviation is combined with the error covariance to establish the objective function and the attack scheme is acquired by solving an optimization problem.Furthermore,we propose a novel detection algorithm that employs a majority-voting mechanism to determine whether the system is under attack,with decision parameters dynamically adjusted in response to system behavior.This approach enhances sensitivity to stealthy and persistent attacks without increasing the false alarm rate.Simulation results show that the designed leads to about a 41%rise in the trace of error covariance for stable systems and 29%for unstable systems,significantly impairing estimation performance.Concurrently,the proposed detection algorithm enhances the attack detection rate by 33%compared to conventional methods.
基金supported in part by the National Natural Science Foundation of China(61933007,62273087,62273088,U21A2019)the Shanghai Pujiang Program of China(22PJ1400400)+2 种基金the Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Royal Society of U.K.the Alexander von Humboldt Foundation of Germany
文摘In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.
文摘Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats.Attackers can non-invasively manipulate sensors and spoof controllers,which in turn increases the autonomy of the system.Even though the focus on protecting against sensor attacks increases,there is still uncertainty about the optimal timing for attack detection.Existing systems often struggle to manage the trade-off between latency and false alarm rate,leading to inefficiencies in real-time anomaly detection.This paper presents a framework designed to monitor,predict,and control dynamic systems with a particular emphasis on detecting and adapting to changes,including anomalies such as“drift”and“attack”.The proposed algorithm integrates a Transformer-based Attention Generative Adversarial Residual model,which combines the strengths of generative adversarial networks,residual networks,and attention algorithms.The system operates in two phases:offline and online.During the offline phase,the proposed model is trained to learn complex patterns,enabling robust anomaly detection.The online phase applies a trained model,where the drift adapter adjusts the model to handle data changes,and the attack detector identifies deviations by comparing predicted and actual values.Based on the output of the attack detector,the controller makes decisions then the actuator executes suitable actions.Finally,the experimental findings show that the proposed model balances detection accuracy of 99.25%,precision of 98.84%,sensitivity of 99.10%,specificity of 98.81%,and an F1-score of 98.96%,thus provides an effective solution for dynamic and safety-critical environments.
文摘The growing intersection of gynecological cancer diagnosis and cybersecurity vulnerabilities in healthcare necessitates integrated solutions that address both diagnostic accuracy and data protection.With increasing reliance on IoT-enabled medical devices,digital twins,and interconnected healthcare systems,the risk of cyberphysical attacks has escalated significantly.Traditional approaches to machine learning(ML)-based diagnosis often lack real-time threat adaptability and privacy preservation,while cybersecurity frameworks fall short in maintaining clinical relevance.This study introduces HealthSecureNet,a novel Cyber-Integrated Predictive Framework designed to detect gynecological cancer and mitigate cybersecurity threats in real time simultaneously.The proposed model employs a three-tier ML architecture incorporating Gradient Boosting and Support Vector Machines(SVMs)for accurate cancer classification,combined with an adaptive anomaly detection layer leveraging Mahalanobis Distance and severity scoring for threat prioritization.To enhance resilience,the framework integrates Zero Trust principles and Federated Learning(FL),enabling secure,decentralized model training while preserving patient privacy and meeting compliance with HIPAA(Health Insurance Portability and Accountability Act)and GDPR(General Data Protection Regulations).Experimental evaluation using a real-world healthcare cybersecurity dataset demonstrated high accuracy(95.2%),precision(94.3%),recall(91.7%),and AUC-ROC(Area Under the Curve-Receiver Operating Characteristic)(0.94),with a low false positive rate(3.6%).HealthSecureNet outperforms traditional models such as SVM,Random Forest(RF),and k-NN(k-Nearest Neighbor)in both anomaly detection and severity classification accuracy.Its adaptive thresholding and response prioritization mechanisms make it suitable for dynamic healthcare environments,enabling early cancer detection and proactive cyber threatmitigationwithout compromising performance or regulatory standards.This research contributes a robust,dual-purpose solution that enhances both clinical diagnostics and cybersecurity,setting a precedent for future AI(Artificial Intelligence)-driven healthcare systems.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
基金supported by the National Natural Science Foundation of China(52332011).
文摘Cyber-physical systems(CPSs)are increasingly vulnerable to cyber-attacks due to their integral connection between cyberspace and the physical world,which is augmented by Internet connectivity.This vulnerability necessitates a heightened focus on developing resilient control mechanisms for CPSs.However,current observer-based active compensation resilient controllers exhibit poor performance against stealthy deception attacks(SDAs)due to the difficulty in accurately reconstructing system states because of the stealthy nature of these attacks.Moreover,some non-active compensation approaches are insufficient when there is a complete loss of actuator control authority.To address these issues,we introduce a novel learning-based passive resilient controller(LPRC).Our approach,unlike observer-based state reconstruction,shows enhanced effectiveness in countering SDAs.We developed a safety state set,represented by an ellipsoid,to ensure CPS stability under SDA conditions,maintaining system trajectories within this set.Additionally,by employing deep reinforcement learning(DRL),the LPRC acquires the capacity to adapt and diverse evolving attack strategies.To empirically substantiate our methodology,various attack methods were compared with current passive and active compensation resilient control methods to evaluate their performance.
基金supported by Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports and Tourism in 2024(Project Name:Global Talent Training Program for Copyright Management Technology in Game Contents,Project Number:RS-2024-00396709,Contribution Rate:100%).
文摘Cyber-physical systems(CPS)represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing,healthcare,and autonomous infrastructure.However,their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats,potentially leading to operational failures and data breaches.Furthermore,CPS faces significant threats related to unauthorized access,improper management,and tampering of the content it generates.In this paper,we propose an intrusion detection system(IDS)optimized for CPS environments using a hybrid approach by combining a natureinspired feature selection scheme,such as Grey Wolf Optimization(GWO),in connection with the emerging Light Gradient Boosting Machine(LightGBM)classifier,named as GWO-LightGBM.While gradient boosting methods have been explored in prior IDS research,our novelty lies in proposing a hybrid approach targeting CPS-specific operational constraints,such as low-latency response and accurate detection of rare and critical attack types.We evaluate GWO-LightGBM against GWO-XGBoost,GWO-CatBoost,and an artificial neural network(ANN)baseline using the NSL-KDD and CIC-IDS-2017 benchmark datasets.The proposed models are assessed across multiple metrics,including accuracy,precision,recall,and F1-score,with an emphasis on class-wise performance and training efficiency.The proposed GWO-LightGBM model achieves the highest overall accuracy(99.73%)for NSL-KDD and(99.61%)for CIC-IDS-2017,demonstrating superior performance in detecting minority classes such as Remote-to-Local(R2L)and Other attacks—commonly overlooked by other classifiers.Moreover,the proposed model consumes lower training time,highlighting its practical feasibility and scalability for real-time CPS deployment.
基金supported by State Administration of Science,Technology and Industry for National Defense,China(No.1000-GEAC0001)
文摘Abstract Performance optimization of cyber-physical systems （CPS） calls for co-design strategies that handle the issues in both computing domain and physical domain. Periods of controller tasks integrated into a uniprocessor system are related to both control performance and real-time schedu- lability analysis simultaneously. System performance improvement can be achieved by optimizing the periods of controller tasks. This paper extends an existing model to select task periods in real-time for CPS with fixed priority controller tasks scheduled by rate-monotonic algorithm. When all the tasks can be integrated, the analytic solution of the problem is derived by using the method of Lagrange multipliers and gradient descent method is evaluated to be suitable online. To further deal with the condition that the system is overloaded, an integrated method is proposed to select periods of tasks online by selecting a subset of tasks first and then optimizing the periods for them. Experimental results demonstrate that our method yields near-optimal result with a short running time.
文摘With the booming of cyber attacks and cyber criminals against cyber-physical systems(CPSs),detecting these attacks remains challenging.It might be the worst of times,but it might be the best of times because of opportunities brought by machine learning(ML),in particular deep learning(DL).In general,DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data.DL models are adopted quickly to cyber attacks against CPS systems.In this survey,a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context.A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems.The methodology includes CPS scenario analysis,cyber attack identification,ML problem formulation,DL model customization,data acquisition for training,and performance evaluation.The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules.Moreover,excellent performance is achieved partly because of several highquality datasets that are readily available for public use.Furthermore,challenges,opportunities,and research trends are pointed out for future research.
基金This work was supported in part by the Natural Science Foundation of China （Nos. 61321002, 61120106010, 61522303, U1509215）, the Program for New Century Excellent Talents in University （No. NCET-13-0045）, and the Beijing Higher Education Young Elite Teacher Project.
文摘Cyber-physical systems （CPSs） are integrations of computation, communication, control and physical processes. Typical examples where CPSs are deployed include smart grids, civil infrastructure, medical devices and manufacturing. Security is one of the most important issues that should be investigated in CPSs and hence has received much attention in recent years. This paper surveys recent results in this area and mainly focusses on three important categories： attack detection, attack design and secure estimation and control. We also discuss several future research directions including risk assessment, modeling of attacks and attacks design, counter-attack strategy and testbed and validation.
文摘With the concepts of Industry 4.0 and smart manufacturing gaining popularity,there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm,targeting innovation,automation,better response to customer needs,and intelligent systems.Within this context,this review focuses on the concept of cyber–physical production system(CPPS)and presents a holistic perspective on the role of the CPPS in three key and essential drivers of this transformation:data-driven manufacturing,decentralized manufacturing,and integrated blockchains for data security.The paper aims to connect these three aspects of smart manufacturing and proposes that through the application of data-driven modeling,CPPS will aid in transforming manufacturing to become more intuitive and automated.In turn,automated manufacturing will pave the way for the decentralization of manufacturing.Layering blockchain technologies on top of CPPS will ensure the reliability and security of data sharing and integration across decentralized systems.Each of these claims is supported by relevant case studies recently published in the literature and from the industry;a brief on existing challenges and the way forward is also provided.
