A definition of self-determined priority is used in airfight decision firstly. A scheme of grouping the whole fighters is introduced, and the principle of target assignment and fire control is designed. Based on the ...A definition of self-determined priority is used in airfight decision firstly. A scheme of grouping the whole fighters is introduced, and the principle of target assignment and fire control is designed. Based on the neutral network, the decision algorithm is derived and the whole coordinated decision system is simulated. Secondly an algorithm for missile-attacking area is described and its calculational result is obtained under initial conditions. Then the attacking of missile is realized by the proportion guidance. Finally, a multi-target attack system. The system includes airfight decision, estimation of missile attack area and calculation of missile attack procedure. A digital simulation demonstrates that the airfight decision algorithm is correct. The methods have important reference values for the study of fire control system of the fourth generation fighter.展开更多
Deep neural networks(DNNs)are widely adopted in daily life and the security problems of DNNs have drawn attention from both scientific researchers and industrial engineers.Many related works show that DNNs are vulnera...Deep neural networks(DNNs)are widely adopted in daily life and the security problems of DNNs have drawn attention from both scientific researchers and industrial engineers.Many related works show that DNNs are vulnerable to adversarial examples that are generated with subtle perturbation to original images in both digital domain and physical domain.As a most common application of DNNs,face recognition systems are likely to cause serious consequences if they are attacked by the adversarial examples.In this paper,we implement an adversarial attack system for face recognition in both digital domain that generates adversarial face images to fool the recognition system,and physical domain that generates customized glasses to fool the system when a person wears the glasses.Experiments show that our system attacks face recognition systems effectively.Furthermore,our system could misguide the recognition system to identify a person wearing the customized glasses as a certain target.We hope this research could help raise the attention of artificial intelligence security and promote building robust recognition systems.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional m...This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional methods by considering both denial-of-service(DoS)and false data injection(FDI)attacks simultaneously.Additionally,the stability conditions for the system under these hybrid attacks are established.It is technically challenging to design the control strategy by predicting attacker actions based on Stcakelberg game to ensure the system stability under hybrid attacks.Another technical difficulty lies in establishing the conditions for mean-square asymptotic stability due to the complexity of the attack scenarios Finally,simulations on an unstable batch reactor system under hybrid attacks demonstrate the effectiveness of the proposed strategy.展开更多
Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by de...Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels.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
Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networ...Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networked predictive control(NPC)approach,an event-based NPC method is proposed.Within the proposed method,the negative effects of time-varying delays and DoS attacks on system performance are compensated.Then,sufficient and necessary conditions are derived to ensure the stability of the closed-loop system.In the end,simulation results are provided to demonstrate the validity of presented method.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
This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded(UBB)noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets.First,properties...This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded(UBB)noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets.First,properties of constrained polynomial zonotopes are provided and the order reduction method is given to reduce the computational complexity.Then,the corresponding improved prediction-update algorithm is proposed so that it can be adapted to non-convex sets.Based on generalized intersection,the utilization of set-based estimation for attack detection is analyzed.Finally,an example is given to show the efficiency of our results.展开更多
Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded...Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.展开更多
Decentralized finance(DeFi)has revolutionized traditional financial paradigms by enabling innovative,permissionless financial transactions.Among these,flash loans represent a significant breakthrough,offering rapid li...Decentralized finance(DeFi)has revolutionized traditional financial paradigms by enabling innovative,permissionless financial transactions.Among these,flash loans represent a significant breakthrough,offering rapid liquidity without collateral requirements.However,the very features that make flash loans appealing also expose DeFi ecosystems to severe security threats.This paper presents a systematic analysis of flash loan attack methodologies,their implications,and potential countermeasures.We formalize the problem via a game-theoretic model,delineating the interactions between malicious actors and security mechanisms.Through detailed case studies of major flash loan attacks,we illustrate common exploit strategies and vulnerabilities within smart contracts.Furthermore,we propose a comprehensive,multilayered security framework that integrates real-time anomaly detection,enhanced smart contract verification,decentralized governance improvements,and cross-platform intelligence sharing.Empirical analysis leveraging blockchain security datasets underscores the viability of these mitigative measures.Our findings contribute to the broader discourse on DeFi security by providing a structured approach to mitigating the systemic risks associated with flash loans,thereby enhancing the resilience of decentralized financial systems.展开更多
Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enha...Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enhance their practicality.Data privacy and security are widely discussed,and many solutions are proposed in this area.Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks.The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems,which is commonly solved via fog computing.This work proposes an energytheft detection method for smart grid systems employed in a fog-based network infrastructure.This work also proposes and analyzes Zero-day energy theft attack detection through a multi-layered approach.The detection process occurs at fog nodes via five machine-learning classification models.The performance of the classifiers is measured,validated,and reported for all models at fog nodes,as well as the required training and testing time.Finally,the measured results are compared to when the detection process occurs at a central processing unit(cloud server)to investigate and compare the performance metrics’goodness.The results show comparable accuracy,precision,recall,and F1-measure performance.Meanwhile,the measured execution time has decreased significantly in the case of the fog-based network infrastructure.The fog-based model achieved an accuracy and recall of 98%,F1 score of 99%,and reduced detection time up to around 85%compared to the cloud-based approach.展开更多
Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber syst...Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber systems[1],[2],which has to face up to malicious cyber-attacks towards cyber communication of control commands.Specifically,jamming attack is regarded as one of the most common attacks of decreasing network performance.Game theory is widely regarded as a method of accurately describing the interaction between jamming attacker and legitimate user[3].In the cyber layer,the signal game model has been utilized to describe the transmission between the attacker and defender[4].However,most previous game theoretical researches are not feasible to meet the demands of industrial CPSs mainly due to the shared communication network nature.Specifically,it leads to incomplete information for players of game owing to various network-induced phenomena and employed communication protocols.In the physical layer,the secure control[5]and estimation[6]under attack detection have been studied for CPSs.However,these methods not only rely heavily on signals injection detection,but also have no access to smart attackers who launch covert attacks so that data receivers cannot observe the attack behaviour[7].Accordingly,the motivation arising here is to tackle the nested game problem for CPSs subject to jamming attack.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
This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates senso...This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.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
A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on ...A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.展开更多
In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mec...In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mechanisms during aggregation,it is difficult to conduct effective backdoor attacks.In addition,existing backdoor attack methods are faced with challenges,such as low backdoor accuracy,poor ability to evade anomaly detection,and unstable model training.To address these challenges,a method called adaptive simulation backdoor attack(ASBA)is proposed.Specifically,ASBA improves the stability of model training by manipulating the local training process and using an adaptive mechanism,the ability of the malicious model to evade anomaly detection by combing large simulation training and clipping,and the backdoor accuracy by introducing a stimulus model to amplify the impact of the backdoor in the global model.Extensive comparative experiments under five advanced defense scenarios show that ASBA can effectively evade anomaly detection and achieve high backdoor accuracy in the global model.Furthermore,it exhibits excellent stability and effectiveness after multiple rounds of attacks,outperforming state-of-the-art backdoor attack methods.展开更多
The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integra...The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.展开更多
Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global...Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated.展开更多
文摘A definition of self-determined priority is used in airfight decision firstly. A scheme of grouping the whole fighters is introduced, and the principle of target assignment and fire control is designed. Based on the neutral network, the decision algorithm is derived and the whole coordinated decision system is simulated. Secondly an algorithm for missile-attacking area is described and its calculational result is obtained under initial conditions. Then the attacking of missile is realized by the proportion guidance. Finally, a multi-target attack system. The system includes airfight decision, estimation of missile attack area and calculation of missile attack procedure. A digital simulation demonstrates that the airfight decision algorithm is correct. The methods have important reference values for the study of fire control system of the fourth generation fighter.
基金This work is supported in part by the National Natural Science Foundation of China under Grant 61902082,U1636215the Guangdong Province Key research and Development Plan under Grant 2019B010136003.
文摘Deep neural networks(DNNs)are widely adopted in daily life and the security problems of DNNs have drawn attention from both scientific researchers and industrial engineers.Many related works show that DNNs are vulnerable to adversarial examples that are generated with subtle perturbation to original images in both digital domain and physical domain.As a most common application of DNNs,face recognition systems are likely to cause serious consequences if they are attacked by the adversarial examples.In this paper,we implement an adversarial attack system for face recognition in both digital domain that generates adversarial face images to fool the recognition system,and physical domain that generates customized glasses to fool the system when a person wears the glasses.Experiments show that our system attacks face recognition systems effectively.Furthermore,our system could misguide the recognition system to identify a person wearing the customized glasses as a certain target.We hope this research could help raise the attention of artificial intelligence security and promote building robust recognition systems.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
基金supported in part by Shanghai Rising-Star Program,China under grant 22QA1409400in part by National Natural Science Foundation of China under grant 62473287 and 62088101in part by Shanghai Municipal Science and Technology Major Project under grant 2021SHZDZX0100.
文摘This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional methods by considering both denial-of-service(DoS)and false data injection(FDI)attacks simultaneously.Additionally,the stability conditions for the system under these hybrid attacks are established.It is technically challenging to design the control strategy by predicting attacker actions based on Stcakelberg game to ensure the system stability under hybrid attacks.Another technical difficulty lies in establishing the conditions for mean-square asymptotic stability due to the complexity of the attack scenarios Finally,simulations on an unstable batch reactor system under hybrid attacks demonstrate the effectiveness of the proposed strategy.
基金supported in part by the National Science Foundation of China(62373240,62273224,U24A20259).
文摘Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels.
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.
基金supported by the National Natural Science Foundation of China(61433003,60904003,11602019).
文摘Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networked predictive control(NPC)approach,an event-based NPC method is proposed.Within the proposed method,the negative effects of time-varying delays and DoS attacks on system performance are compensated.Then,sufficient and necessary conditions are derived to ensure the stability of the closed-loop system.In the end,simulation results are provided to demonstrate the validity of presented method.
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(61703286,62394342,61890924,61991404)。
文摘This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded(UBB)noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets.First,properties of constrained polynomial zonotopes are provided and the order reduction method is given to reduce the computational complexity.Then,the corresponding improved prediction-update algorithm is proposed so that it can be adapted to non-convex sets.Based on generalized intersection,the utilization of set-based estimation for attack detection is analyzed.Finally,an example is given to show the efficiency of our results.
基金supported by the National Natural Science Foundation of China(62303273,62373226)the National Research Foundation,Singapore through the Medium Sized Center for Advanced Robotics Technology Innovation(WP2.7)
文摘Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.
基金supported by the Sichuan Science and Technology Program(2024YFHZ0161).
文摘Decentralized finance(DeFi)has revolutionized traditional financial paradigms by enabling innovative,permissionless financial transactions.Among these,flash loans represent a significant breakthrough,offering rapid liquidity without collateral requirements.However,the very features that make flash loans appealing also expose DeFi ecosystems to severe security threats.This paper presents a systematic analysis of flash loan attack methodologies,their implications,and potential countermeasures.We formalize the problem via a game-theoretic model,delineating the interactions between malicious actors and security mechanisms.Through detailed case studies of major flash loan attacks,we illustrate common exploit strategies and vulnerabilities within smart contracts.Furthermore,we propose a comprehensive,multilayered security framework that integrates real-time anomaly detection,enhanced smart contract verification,decentralized governance improvements,and cross-platform intelligence sharing.Empirical analysis leveraging blockchain security datasets underscores the viability of these mitigative measures.Our findings contribute to the broader discourse on DeFi security by providing a structured approach to mitigating the systemic risks associated with flash loans,thereby enhancing the resilience of decentralized financial systems.
文摘Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enhance their practicality.Data privacy and security are widely discussed,and many solutions are proposed in this area.Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks.The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems,which is commonly solved via fog computing.This work proposes an energytheft detection method for smart grid systems employed in a fog-based network infrastructure.This work also proposes and analyzes Zero-day energy theft attack detection through a multi-layered approach.The detection process occurs at fog nodes via five machine-learning classification models.The performance of the classifiers is measured,validated,and reported for all models at fog nodes,as well as the required training and testing time.Finally,the measured results are compared to when the detection process occurs at a central processing unit(cloud server)to investigate and compare the performance metrics’goodness.The results show comparable accuracy,precision,recall,and F1-measure performance.Meanwhile,the measured execution time has decreased significantly in the case of the fog-based network infrastructure.The fog-based model achieved an accuracy and recall of 98%,F1 score of 99%,and reduced detection time up to around 85%compared to the cloud-based approach.
基金supported by the National Natural Science Foundation of China(62173136)the Natural Science Foundation of Hunan Province(2020JJ2013,2021JJ50047).
文摘Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber systems[1],[2],which has to face up to malicious cyber-attacks towards cyber communication of control commands.Specifically,jamming attack is regarded as one of the most common attacks of decreasing network performance.Game theory is widely regarded as a method of accurately describing the interaction between jamming attacker and legitimate user[3].In the cyber layer,the signal game model has been utilized to describe the transmission between the attacker and defender[4].However,most previous game theoretical researches are not feasible to meet the demands of industrial CPSs mainly due to the shared communication network nature.Specifically,it leads to incomplete information for players of game owing to various network-induced phenomena and employed communication protocols.In the physical layer,the secure control[5]and estimation[6]under attack detection have been studied for CPSs.However,these methods not only rely heavily on signals injection detection,but also have no access to smart attackers who launch covert attacks so that data receivers cannot observe the attack behaviour[7].Accordingly,the motivation arising here is to tackle the nested game problem for CPSs subject to jamming attack.
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.
文摘This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.
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A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.
文摘In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mechanisms during aggregation,it is difficult to conduct effective backdoor attacks.In addition,existing backdoor attack methods are faced with challenges,such as low backdoor accuracy,poor ability to evade anomaly detection,and unstable model training.To address these challenges,a method called adaptive simulation backdoor attack(ASBA)is proposed.Specifically,ASBA improves the stability of model training by manipulating the local training process and using an adaptive mechanism,the ability of the malicious model to evade anomaly detection by combing large simulation training and clipping,and the backdoor accuracy by introducing a stimulus model to amplify the impact of the backdoor in the global model.Extensive comparative experiments under five advanced defense scenarios show that ASBA can effectively evade anomaly detection and achieve high backdoor accuracy in the global model.Furthermore,it exhibits excellent stability and effectiveness after multiple rounds of attacks,outperforming state-of-the-art backdoor attack methods.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.(GPIP:1074-612-2024).
文摘The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.
基金supported by the National Natural Science Foundation of China(Grant No.62172123)the Key Research and Development Program of Heilongjiang Province,China(GrantNo.2022ZX01A36).
文摘Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated.
