This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimati...This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimation structure under consideration,an estimation center is not necessary,and the estimator derives its information from itself and neighboring nodes,which fuses the state vector and the measurement vector.In an effort to cut down data conflicts in communication networks,the stochastic communication protocol(SCP)is employed so that the output signals from sensors can be selected.Additionally,a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data.On this basis,sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error.Finally,a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.展开更多
This paper investigates the adaptive neural network(NN)event-triggered secure formation control problem for nonholonomic mobile robots(NMRs)subject to deception attacks.The NNs are employed to approximate unknown nonl...This paper investigates the adaptive neural network(NN)event-triggered secure formation control problem for nonholonomic mobile robots(NMRs)subject to deception attacks.The NNs are employed to approximate unknown nonlinear functions in robotic dynamics.Since the transmission channel from sensor-to-controller is vulnerable to deception attacks,a NN estimation technique is introduced to estimate the unknown deception attacks.In order to alleviate the amount of communication between controller-and-actuator,an event-triggered mechanism with relative threshold strategy is established.Then,an adaptive NN event-triggered secure formation control method is proposed.It is proved that all closed-loop signals of controlled systems are bounded and the formation tracking errors converge a neighborhood of the origin in the presence of deception attacks.The comparative simulations illustrate the effectiveness of the proposed secure formation control scheme.展开更多
Cyber attacks pose severe threats on synchronization of multi-agent systems.Deception attack,as a typical type of cyber attack,can bypass the surveillance of the attack detection mechanism silently,resulting in a heav...Cyber attacks pose severe threats on synchronization of multi-agent systems.Deception attack,as a typical type of cyber attack,can bypass the surveillance of the attack detection mechanism silently,resulting in a heavy loss.Therefore,the problem of mean-square bounded synchronization in multi-agent systems subject to deception attacks is investigated in this paper.The control signals can be replaced with false data from controllerto-actuator channels or the controller.The success of the attack is measured through a stochastic variable.A distributed impulsive controller using a pinning strategy is redesigned,which ensures that mean-square bounded synchronization is achieved in the presence of deception attacks.Some sufficient conditions are derived,in which upper bounds of the synchronization error are given.Finally,two numerical simulations with symmetric and asymmetric network topologies are given to illustrate the theoretical results.展开更多
Without the known state equation, a new state estimation strategy is designed to be against malicious attacks for cyber physical systems. Inspired by the idea of data reconstruction, the compressive sensing （CS） is ...Without the known state equation, a new state estimation strategy is designed to be against malicious attacks for cyber physical systems. Inspired by the idea of data reconstruction, the compressive sensing （CS） is applied to reconstruction of residual measurements after the detection and identification scheme based on the Markov graph of the system state, which increases the resilience of state estimation strategy against deception attacks. First, the observability analysis is introduced to decide the triggering time of the measurement reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-singular value decomposition （K-SVD）, which is produced adaptively according to the characteristics of the measurement data. In addition, due to the irregularity of residual measurements, a sampling matrix is designed as the measurement matrix. Finally, the simulation experiments are performed on 6-bus power system. Results show that the reconstruction of measurements is completed well by the proposed reconstruction method, and the corresponding effects are better than reconstruction scheme based on the joint dictionary and the traditional Gauss or Bernoulli random matrix respectively. Especially, when only 29% available clean measurements are left, performance of the proposed strategy is still extraordinary, which reflects generality for five kinds of recovery algorithms.展开更多
This paper examines the bipartite bounded consensus of multiagent systems(MASs)connected by signed graphs.The considered MAS includes a virtual leader and multiple followers with nonlinear dynamics,where communication...This paper examines the bipartite bounded consensus of multiagent systems(MASs)connected by signed graphs.The considered MAS includes a virtual leader and multiple followers with nonlinear dynamics,where communication link weights between neighboring agents can be negative.To achieve consensus,impulsive control depending on neighbor information is utilized.However,this control may be subjected to deception attacks.To optimize control efficiency by reducing frequency and shortening consensus time,a self-triggered mechanism that determines impulsive instants with variable intervals is proposed.Utilizing graph theory,linear matrix inequality(LMI),and the Lyapunov functional method,conditions for achieving bipartite bounded consensus and the consensus error bound are provided.This study reveals that the graph topology,attack probability,and the maximum value of impulsive intervals are key factors affecting the consensus.Numerical simulations validate the theoretical findings.A comparison of strategies with fixed and self-triggered impulsive intervals highlights the effectiveness of the selftriggered scheme.展开更多
This paper focuses on the design of event-triggered controllers for the synchronization of delayed Takagi-Sugeno(T-S)fuzzy neural networks(NNs)under deception attacks.The traditional event-triggered mechanism(ETM)dete...This paper focuses on the design of event-triggered controllers for the synchronization of delayed Takagi-Sugeno(T-S)fuzzy neural networks(NNs)under deception attacks.The traditional event-triggered mechanism(ETM)determines the next trigger based on the current sample,resulting in network congestion.Furthermore,such methods suffer from the issues of deception attacks and unmeasurable system states.To enhance the system stability,we adaptively detect the occurrence of events over a period of time.In addition,deception attacks are recharacterized to describe general scenarios.Specifically,the following enhancements are implemented:First,we use a Bernoulli process to model the occurrence of deception attacks,which can describe a variety of attack scenarios as a type of general Markov process.Second,we introduce a sum-based dynamic discrete event-triggered mechanism(SDDETM),which uses a combination of past sampled measurements and internal dynamic variables to determine subsequent triggering events.Finally,we incorporate a dynamic output feedback controller(DOFC)to ensure the system stability.The concurrent design of the DOFC and SDDETM parameters is achieved through the application of the cone complement linearization(CCL)algorithm.We further perform two simulation examples to validate the effectiveness of the algorithm.展开更多
This paper proposes a model-based control framework for vehicle platooning systems with secondorder nonlinear dynamics operating over switching signed networks,time-varying delays,and deception attacks.The study inclu...This paper proposes a model-based control framework for vehicle platooning systems with secondorder nonlinear dynamics operating over switching signed networks,time-varying delays,and deception attacks.The study includes two configurations:a leaderless structure using Finite-Time Non-Singular Terminal Bipartite Consensus(FNTBC)and Fixed-Time Bipartite Consensus(FXTBC),and a leader—follower structure ensuring structural balance and robustness against deceptive signals.In the leaderless model,a bipartite controller based on impulsive control theory,gauge transformation,and Markovian switching Lyapunov functions ensures mean-square stability and coordination under deception attacks and communication delays.The FNTBC achieves finite-time convergence depending on initial conditions,while the FXTBC guarantees fixed-time convergence independent of them,providing adaptability to different operating states.In the leader—follower case,a discontinuous impulsive control law synchronizes all followers with the leader despite deceptive attacks and switching topologies,maintaining robust coordination through nonlinear corrective mechanisms.To validate the approach,simulations are conducted on systems of five and seventeen vehicles in both leaderless and leader—follower configurations.The results demonstrate that the proposed framework achieves rapid consensus,strong robustness,and high resistance to deception attacks,offering a secure and scalable model-based control solution for modern vehicular communication networks.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 article,we study the secure control of the Markovian jumping neural networks(MJNNs)subject to deception attacks.Considering the limitation of the network bandwidth and the impact of the deception attacks,we pr...In this article,we study the secure control of the Markovian jumping neural networks(MJNNs)subject to deception attacks.Considering the limitation of the network bandwidth and the impact of the deception attacks,we propose two memory-based adaptive event-trigger mechanisms(AETMs).Different from the available event-trigger mechanisms,these two memory-based AETMs contain the historical triggered data not only in the triggering conditions,but also in the adaptive law.They can adjust the data transmission rate adaptively so as to alleviate the impact of deception attacks on the controlled system and to suppress the peak of the system response.In view of the proposed memory-based AETMs,a time-dependent Lyapunov functional is constructed to analyze the stability of the error system.Some sufficient conditions to ensure the asymptotical synchronization of master-slave MJNNs are obtained,and two easy-to-implement co-design algorithms for the feedback gain matrix and the trigger matrix are given.Finally,a numerical example is given to verify the feasibility and superiority of the two memory-based AETMs.展开更多
Industrial robots are becoming increasingly vulnerable to cyber incidents and attacks,particularly with the dawn of the Industrial Internet-of-Things(IIoT).To gain a comprehensive understanding of these cyber risks,vu...Industrial robots are becoming increasingly vulnerable to cyber incidents and attacks,particularly with the dawn of the Industrial Internet-of-Things(IIoT).To gain a comprehensive understanding of these cyber risks,vulnerabilities of industrial robots were analyzed empirically,using more than three million communication packets collected with testbeds of two ABB IRB120 robots and five other robots from various original equipment manufacturers(OEMs).This analysis,guided by the confidentiality-integrity-availability(CIA)triad,uncovers robot vulnerabilities in three dimensions:confidentiality,integrity,and availability.These vulnerabilities were used to design Covering Robot Manipulation via Data Deception(CORMAND2),an automated cyber-physical attack against industrial robots.CORMAND2 manipulates robot operation while deceiving the Supervisory Control and Data Acquisition(SCADA)system that the robot is operating normally by modifying the robot’s movement data and data deception.CORMAND2 and its capability of degrading the manufacturing was validated experimentally using the aforementioned seven robots from six different OEMs.CORMAND2 unveils the limitations of existing anomaly detection systems,more specifically the assumption of the authenticity of SCADA-received movement data,to which we propose mitigations for.展开更多
This paper discusses the design of resilient and event-triggered control for linear aperiodic sampled-data systems.The stability and stabilization problem of the aperiodic sampled-data systems under a dynamic event-tr...This paper discusses the design of resilient and event-triggered control for linear aperiodic sampled-data systems.The stability and stabilization problem of the aperiodic sampled-data systems under a dynamic event-triggered scheme and against a stochastic deception attack is addressed in a novel looped-functional framework.A quadratic event-triggered scheme with a discrete-time dynamic variable is proposed in which the system states are only evaluated at aperiodic sampling instants so that the Zeno phenomenon can be avoided consequently.The system is assumed to be intruded by a deception attack signal which is determined by a Bernoulli random variable.Our objective in this paper is to derive the stability conditions firstly and then provide the resilient and event-triggered controller design for the aperiodic sampled-data system.With a certain H∞attack and the control updates can be obviously reduced by the proposed dynamic event-triggered scheme,which means the system performance,the limited communication resources,and the system security can be well balanced in our design.Finally,the validity and effectiveness of the proposed method is demonstrated by the simulations.展开更多
Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making capabilities.Nowadays,MASs have a wide range of applications in smart gri...Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making capabilities.Nowadays,MASs have a wide range of applications in smart grids,smart manufacturing,sensor networks,and intelligent transportation systems.Control of the MASs are often coordinated through information interaction among agents,which is one of the most important factors affecting coordination and cooperation performance.However,unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly,and thus could lead to severe degradation of the whole system performance and even destruction of MASs.This paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks,and our goal is to present a comprehensive survey on recent results on fault estimation,detection,diagnosis and fault-tolerant control of MASs,and cyber attack detection and secure control of MASs subject to two typical cyber attacks.Finally,the paper concludes with some potential future research topics on the security issues of MASs.展开更多
In this work,an H_(∞)/passive-based secure synchronization control problem is investigated for continuous-time semi-Markov neural networks subject to hybrid attacks,in which hybrid attacks are the combinations of den...In this work,an H_(∞)/passive-based secure synchronization control problem is investigated for continuous-time semi-Markov neural networks subject to hybrid attacks,in which hybrid attacks are the combinations of denial-of-service attacks and deception attacks,and they are described by two groups of independent Bernoulli distributions.On this foundation,via the Lyapunov stability theory and linear matrix inequality technology,the H_(∞)/passive-based performance criteria for semi-Markov jump neural networks are obtained.Additionally,an activation function division approach for neural networks is adopted to further reduce the conservatism of the criteria.Finally,a simulation example is provided to verify the validity and feasibility of the proposed method.展开更多
基金supported in part by the National Natural Science Foundation of China(62073189,62173207)the Taishan Scholar Project of Shandong Province(tsqn202211129)。
文摘This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimation structure under consideration,an estimation center is not necessary,and the estimator derives its information from itself and neighboring nodes,which fuses the state vector and the measurement vector.In an effort to cut down data conflicts in communication networks,the stochastic communication protocol(SCP)is employed so that the output signals from sensors can be selected.Additionally,a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data.On this basis,sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error.Finally,a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.
基金supported by the National Natural Science Foundation of China under 62173172.
文摘This paper investigates the adaptive neural network(NN)event-triggered secure formation control problem for nonholonomic mobile robots(NMRs)subject to deception attacks.The NNs are employed to approximate unknown nonlinear functions in robotic dynamics.Since the transmission channel from sensor-to-controller is vulnerable to deception attacks,a NN estimation technique is introduced to estimate the unknown deception attacks.In order to alleviate the amount of communication between controller-and-actuator,an event-triggered mechanism with relative threshold strategy is established.Then,an adaptive NN event-triggered secure formation control method is proposed.It is proved that all closed-loop signals of controlled systems are bounded and the formation tracking errors converge a neighborhood of the origin in the presence of deception attacks.The comparative simulations illustrate the effectiveness of the proposed secure formation control scheme.
基金supported by the National Natural Science Foundation of China(61988101,61922030,61773163)Shanghai Rising-Star Program(18QA1401400)+3 种基金the International(Regional)Cooperation and Exchange Project(61720106008)the Natural Science Foundation of Shanghai(17ZR1406800)the Fundamental Research Funds for the Central Universitiesthe 111 Project(B17017)。
文摘Cyber attacks pose severe threats on synchronization of multi-agent systems.Deception attack,as a typical type of cyber attack,can bypass the surveillance of the attack detection mechanism silently,resulting in a heavy loss.Therefore,the problem of mean-square bounded synchronization in multi-agent systems subject to deception attacks is investigated in this paper.The control signals can be replaced with false data from controllerto-actuator channels or the controller.The success of the attack is measured through a stochastic variable.A distributed impulsive controller using a pinning strategy is redesigned,which ensures that mean-square bounded synchronization is achieved in the presence of deception attacks.Some sufficient conditions are derived,in which upper bounds of the synchronization error are given.Finally,two numerical simulations with symmetric and asymmetric network topologies are given to illustrate the theoretical results.
基金This work was supported by the Natural Science Foundation of China （NSFC）-Guangdong Joint Foundation Key Project （No. U1401253）, the NSFC （Nos. 61573153, 616721 74）, the Foundation of Guangdong Provincial Science and Technology Projects （No. 2013B010401001 ）, the Fundamental Research Funds for the Central Universities （No. 2015ZZ099）, the Guangzhou Science and Technology Plan Project （No. 201510010132）, the Maoming Science and Technology Plan Project （No. MM201 7000004）, and the National Natural Science Foundation of Guangdong Province （No. 2016A030313510）.
文摘Without the known state equation, a new state estimation strategy is designed to be against malicious attacks for cyber physical systems. Inspired by the idea of data reconstruction, the compressive sensing （CS） is applied to reconstruction of residual measurements after the detection and identification scheme based on the Markov graph of the system state, which increases the resilience of state estimation strategy against deception attacks. First, the observability analysis is introduced to decide the triggering time of the measurement reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-singular value decomposition （K-SVD）, which is produced adaptively according to the characteristics of the measurement data. In addition, due to the irregularity of residual measurements, a sampling matrix is designed as the measurement matrix. Finally, the simulation experiments are performed on 6-bus power system. Results show that the reconstruction of measurements is completed well by the proposed reconstruction method, and the corresponding effects are better than reconstruction scheme based on the joint dictionary and the traditional Gauss or Bernoulli random matrix respectively. Especially, when only 29% available clean measurements are left, performance of the proposed strategy is still extraordinary, which reflects generality for five kinds of recovery algorithms.
基金supported by the National Natural Science Foundation of China(Grant No.62373165)the Natural Science Foundation of Jiangsu Province(Grant No.BK20181342)。
文摘This paper examines the bipartite bounded consensus of multiagent systems(MASs)connected by signed graphs.The considered MAS includes a virtual leader and multiple followers with nonlinear dynamics,where communication link weights between neighboring agents can be negative.To achieve consensus,impulsive control depending on neighbor information is utilized.However,this control may be subjected to deception attacks.To optimize control efficiency by reducing frequency and shortening consensus time,a self-triggered mechanism that determines impulsive instants with variable intervals is proposed.Utilizing graph theory,linear matrix inequality(LMI),and the Lyapunov functional method,conditions for achieving bipartite bounded consensus and the consensus error bound are provided.This study reveals that the graph topology,attack probability,and the maximum value of impulsive intervals are key factors affecting the consensus.Numerical simulations validate the theoretical findings.A comparison of strategies with fixed and self-triggered impulsive intervals highlights the effectiveness of the selftriggered scheme.
基金Project supported by the National Natural Science Foundation of China(Nos.T2121002,62473321,62403014,and 62233001)。
文摘This paper focuses on the design of event-triggered controllers for the synchronization of delayed Takagi-Sugeno(T-S)fuzzy neural networks(NNs)under deception attacks.The traditional event-triggered mechanism(ETM)determines the next trigger based on the current sample,resulting in network congestion.Furthermore,such methods suffer from the issues of deception attacks and unmeasurable system states.To enhance the system stability,we adaptively detect the occurrence of events over a period of time.In addition,deception attacks are recharacterized to describe general scenarios.Specifically,the following enhancements are implemented:First,we use a Bernoulli process to model the occurrence of deception attacks,which can describe a variety of attack scenarios as a type of general Markov process.Second,we introduce a sum-based dynamic discrete event-triggered mechanism(SDDETM),which uses a combination of past sampled measurements and internal dynamic variables to determine subsequent triggering events.Finally,we incorporate a dynamic output feedback controller(DOFC)to ensure the system stability.The concurrent design of the DOFC and SDDETM parameters is achieved through the application of the cone complement linearization(CCL)algorithm.We further perform two simulation examples to validate the effectiveness of the algorithm.
基金Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP.2/103/46”Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia for funding this research work through project number“NBU-FFR-2025-871-15”funding from Prince Sattam bin Abdulaziz University project number(PSAU/2025/R/1447).
文摘This paper proposes a model-based control framework for vehicle platooning systems with secondorder nonlinear dynamics operating over switching signed networks,time-varying delays,and deception attacks.The study includes two configurations:a leaderless structure using Finite-Time Non-Singular Terminal Bipartite Consensus(FNTBC)and Fixed-Time Bipartite Consensus(FXTBC),and a leader—follower structure ensuring structural balance and robustness against deceptive signals.In the leaderless model,a bipartite controller based on impulsive control theory,gauge transformation,and Markovian switching Lyapunov functions ensures mean-square stability and coordination under deception attacks and communication delays.The FNTBC achieves finite-time convergence depending on initial conditions,while the FXTBC guarantees fixed-time convergence independent of them,providing adaptability to different operating states.In the leader—follower case,a discontinuous impulsive control law synchronizes all followers with the leader despite deceptive attacks and switching topologies,maintaining robust coordination through nonlinear corrective mechanisms.To validate the approach,simulations are conducted on systems of five and seventeen vehicles in both leaderless and leader—follower configurations.The results demonstrate that the proposed framework achieves rapid consensus,strong robustness,and high resistance to deception attacks,offering a secure and scalable model-based control solution for modern vehicular communication networks.
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 by the National Natural Science Foundation of China (Grant Nos.61973199,62003794,and 62173214)the Shandong Provincial Natural Science Foundation (Grant Nos.ZR2020QF050 and ZR2021MF003)the Taishan Scholar Project of Shandong Province of China。
文摘In this article,we study the secure control of the Markovian jumping neural networks(MJNNs)subject to deception attacks.Considering the limitation of the network bandwidth and the impact of the deception attacks,we propose two memory-based adaptive event-trigger mechanisms(AETMs).Different from the available event-trigger mechanisms,these two memory-based AETMs contain the historical triggered data not only in the triggering conditions,but also in the adaptive law.They can adjust the data transmission rate adaptively so as to alleviate the impact of deception attacks on the controlled system and to suppress the peak of the system response.In view of the proposed memory-based AETMs,a time-dependent Lyapunov functional is constructed to analyze the stability of the error system.Some sufficient conditions to ensure the asymptotical synchronization of master-slave MJNNs are obtained,and two easy-to-implement co-design algorithms for the feedback gain matrix and the trigger matrix are given.Finally,a numerical example is given to verify the feasibility and superiority of the two memory-based AETMs.
基金Science and Technology Innovation 2030 Program(2018AAA0101605).
文摘Industrial robots are becoming increasingly vulnerable to cyber incidents and attacks,particularly with the dawn of the Industrial Internet-of-Things(IIoT).To gain a comprehensive understanding of these cyber risks,vulnerabilities of industrial robots were analyzed empirically,using more than three million communication packets collected with testbeds of two ABB IRB120 robots and five other robots from various original equipment manufacturers(OEMs).This analysis,guided by the confidentiality-integrity-availability(CIA)triad,uncovers robot vulnerabilities in three dimensions:confidentiality,integrity,and availability.These vulnerabilities were used to design Covering Robot Manipulation via Data Deception(CORMAND2),an automated cyber-physical attack against industrial robots.CORMAND2 manipulates robot operation while deceiving the Supervisory Control and Data Acquisition(SCADA)system that the robot is operating normally by modifying the robot’s movement data and data deception.CORMAND2 and its capability of degrading the manufacturing was validated experimentally using the aforementioned seven robots from six different OEMs.CORMAND2 unveils the limitations of existing anomaly detection systems,more specifically the assumption of the authenticity of SCADA-received movement data,to which we propose mitigations for.
基金supported in part by the China Scholarship Council(No.202206030132)the European Union-NextGenerationEU。
文摘This paper discusses the design of resilient and event-triggered control for linear aperiodic sampled-data systems.The stability and stabilization problem of the aperiodic sampled-data systems under a dynamic event-triggered scheme and against a stochastic deception attack is addressed in a novel looped-functional framework.A quadratic event-triggered scheme with a discrete-time dynamic variable is proposed in which the system states are only evaluated at aperiodic sampling instants so that the Zeno phenomenon can be avoided consequently.The system is assumed to be intruded by a deception attack signal which is determined by a Bernoulli random variable.Our objective in this paper is to derive the stability conditions firstly and then provide the resilient and event-triggered controller design for the aperiodic sampled-data system.With a certain H∞attack and the control updates can be obviously reduced by the proposed dynamic event-triggered scheme,which means the system performance,the limited communication resources,and the system security can be well balanced in our design.Finally,the validity and effectiveness of the proposed method is demonstrated by the simulations.
基金partially supported by the National Natural Science Foundation of China(61873237)the Fundamental Research Funds for the Central Universities+2 种基金the Fundamental Research Funds for the Provincial Universities of Zhejiang(RF-A2019003)the Research Grants Council of the Hong Kong Special Administrative Region of China(City U/11204315)the Hong Kong Scholars Program(XJ2016030)。
文摘Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making capabilities.Nowadays,MASs have a wide range of applications in smart grids,smart manufacturing,sensor networks,and intelligent transportation systems.Control of the MASs are often coordinated through information interaction among agents,which is one of the most important factors affecting coordination and cooperation performance.However,unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly,and thus could lead to severe degradation of the whole system performance and even destruction of MASs.This paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks,and our goal is to present a comprehensive survey on recent results on fault estimation,detection,diagnosis and fault-tolerant control of MASs,and cyber attack detection and secure control of MASs subject to two typical cyber attacks.Finally,the paper concludes with some potential future research topics on the security issues of MASs.
基金supported by the National Natural Science Foundation of China under Grant Nos.62103005,62173001,and 62273006the Natural Science Foundation of Anhui Provincial Natural Science Foundation under Grant No.2108085QF276+3 种基金the Natural Science Foundation for Distinguished Young Scholars of Higher Education Institutions of Anhui Province under Grant No.2022AH020034the Natural Science Foundation for Excellent Young Scholars of Higher Education Institutions of Anhui Province under Grant No.2022AH030049,2023AH030030,2022AH030049the Major Technologies Research and Development Special Program of Anhui Province under Grant No.202003a05020001the Key Research and Development Projects of Anhui Province under Grant No.202104a05020015。
文摘In this work,an H_(∞)/passive-based secure synchronization control problem is investigated for continuous-time semi-Markov neural networks subject to hybrid attacks,in which hybrid attacks are the combinations of denial-of-service attacks and deception attacks,and they are described by two groups of independent Bernoulli distributions.On this foundation,via the Lyapunov stability theory and linear matrix inequality technology,the H_(∞)/passive-based performance criteria for semi-Markov jump neural networks are obtained.Additionally,an activation function division approach for neural networks is adopted to further reduce the conservatism of the criteria.Finally,a simulation example is provided to verify the validity and feasibility of the proposed method.
