Communication plays a vital role in incorporating smartness into the interconnected power system.However,historical records prove that the data transfer has always been vulnerable to cyber-attacks.Unless these cyber-a...Communication plays a vital role in incorporating smartness into the interconnected power system.However,historical records prove that the data transfer has always been vulnerable to cyber-attacks.Unless these cyber-attacks are identified and cordoned off,they may lead to black-out and result in national security issues.This paper proposes an optimal two-stage Kalman filter(OTS-KF)for simultaneous state and cyber-attack estimation in automatic generation control(AGC)system.Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics.Five types of cyber-attacks,i.e.,false data injection(FDI),data replay attack,denial of service(DoS),scaling,and ramp attacks,are injected into the measurements and estimated using OTS-KF.As the load variations of each area are seldom available,OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system.The proposed technique is validated on the benchmark two-area,three-area,and five-area power system models.The simulation results under various test conditions demonstrate the efficacy of the proposed filter.展开更多
The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart grids.For this reason,a resilient active defe...The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart grids.For this reason,a resilient active defense control scheme based on interval observer detection is proposed in this paper to protect smart grids.The proposed active defense highlights the integration of detection and defense against FDIAs in smart girds.First,a dynamic physical grid model under FDIAs is modeled,in which model uncertainty and parameter uncertainty are taken into account.Then,an interval observer-based detection method against FDIAs is proposed,where a detection criteria using interval residual is put forward.Corresponding to the detection results,the resilient defense controller is triggered to defense the FDIAs if the system states are affected by FDIAs.Linear matrix inequality(LMI)approach is applied to design the resilient controller with H_(∞)performance.The system with the resilient defense controller can be robust to FDIAs and the gain of the resilient controller has a certain gain margin.Our active resilient defense approach can be built in real time and show accurate and quick respond to the injected FDIAs.The effectiveness of the proposed defense scheme is verified by the simulation results on an IEEE 30-bus grid system.展开更多
Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system s...Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system state estimation(DSSE).The uncertainty of the distribution network is represented by the interval of each state variable.A three-phase interval DSSE model is proposed to construct the interval of each state variable.An improved iterative algorithm(IIA)is developed to solve the interval DSSE model and to obtain the lower and upper bounds of the interval.A cyber-attack is detected when the value of the state variable estimated by the traditional DSSE is out of the corresponding interval determined by the interval DSSE.To validate the proposed cyber-attack detection strategy,the basic principle of the cyber-attack is studied,and its general model is formulated.The proposed cyber-attack model and detection strategy are conducted on the IEEE 33-bus and 123-bus systems.Comparative experiments of the proposed IIA,Monte Carlo simulation algorithm,and interval Gauss elimination algorithm prove the validation of the proposed method.展开更多
Detecting cyber-attacks undoubtedly has become a big data problem. This paper presents a tutorial on data mining based cyber-attack detection. First,a data driven defence framework is presented in terms of cyber secur...Detecting cyber-attacks undoubtedly has become a big data problem. This paper presents a tutorial on data mining based cyber-attack detection. First,a data driven defence framework is presented in terms of cyber security situational awareness. Then, the process of data mining based cyber-attack detection is discussed. Next,a multi-loop learning architecture is presented for data mining based cyber-attack detection. Finally,common data mining techniques for cyber-attack detection are discussed.展开更多
With the high-speed development of decentralized applications,account-based blockchain platforms have become a hotbed of various financial scams and hacks due to their anonymity and high financial value.Financial secu...With the high-speed development of decentralized applications,account-based blockchain platforms have become a hotbed of various financial scams and hacks due to their anonymity and high financial value.Financial security has become a top priority with the sustainable development of blockchain-based platforms because of an increasing number of cyber attacks,which have resulted in a huge loss of crypto assets in recent years.Therefore,it is imperative to study the real-time detection of cyber attacks to facilitate effective supervision and regulation.To this end,this paper proposes the weighted and extended isolation forest algorithms and designs a novel framework for the real-time detection of cyber-attack transactions by thoroughly studying and summarizing real-world examples.Furthermore,this study develops a new detection approach for locating the compromised address of a cyber attack to resolve the data scarcity of hack addresses and reduce time consumption.Moreover,three experiments are carried out not only to apply on different types of cyber attacks but also to compare the proposed approach with the widely used existing methods.The results demonstrate the high efficiency and generality of the proposed approach.Finally,the lower time consumption and robustness of our method were validated through additional experiments.In conclusion,the proposed blockchain-oriented approach in this study can handle real-time detection of cyber attacks and has significant scope for applications.展开更多
As intelligent vehicles become increasingly computerized and networked,they gain more autonomous capabilities.However,they are also becoming more exposed to cyber-threats which are likely to be a more prominent concer...As intelligent vehicles become increasingly computerized and networked,they gain more autonomous capabilities.However,they are also becoming more exposed to cyber-threats which are likely to be a more prominent concern.This paper proposes a cyber-attack detection method for autonomous vehicles based on secure estimation of vehicle states,with an example application under attacks in the vehicle localization system.To investigate the effects of vehicle model and estimator on the attack detection performance,different nonlinear vehicle dynamic models and estimation approaches are employed.The deviation between the measurement from the onboard sensors and the state estimation is monitored in real time.With the designed vehicle state estimator and preset threshold,the cyber-attack detection algorithm is further developed for autonomous vehicles,whose performance is tested in simulations where the vehicle localization system is assumed to be compromised during a double lane change maneuver.The test results demonstrate the feasibility and effectiveness of the proposed cyber-attack algorithm.In addition,the results illustrate the impacts of vehicle nonlinear characteristics on the cyber-attack detection performance.Beyond this,the effects of different vehicle models on the attack detection performance,as well as the selection of suitable filtering approaches for the attack detection,are also discussed.展开更多
In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of(industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate,fa...In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of(industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate,fast, and robust power system state estimation(PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states(i.e., nodal voltage phasors).Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramér-Rao lower bound is developed.Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.展开更多
文摘Communication plays a vital role in incorporating smartness into the interconnected power system.However,historical records prove that the data transfer has always been vulnerable to cyber-attacks.Unless these cyber-attacks are identified and cordoned off,they may lead to black-out and result in national security issues.This paper proposes an optimal two-stage Kalman filter(OTS-KF)for simultaneous state and cyber-attack estimation in automatic generation control(AGC)system.Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics.Five types of cyber-attacks,i.e.,false data injection(FDI),data replay attack,denial of service(DoS),scaling,and ramp attacks,are injected into the measurements and estimated using OTS-KF.As the load variations of each area are seldom available,OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system.The proposed technique is validated on the benchmark two-area,three-area,and five-area power system models.The simulation results under various test conditions demonstrate the efficacy of the proposed filter.
基金supported by the National Nature Science Foundation of China(Nos.62103357,62203376)the Science and Technology Plan of Hebei Education Department(No.QN2021139)+1 种基金the Nature Science Foundation of Hebei Province(Nos.F2021203043,F2022203074)the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology(No.XTCX202203).
文摘The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart grids.For this reason,a resilient active defense control scheme based on interval observer detection is proposed in this paper to protect smart grids.The proposed active defense highlights the integration of detection and defense against FDIAs in smart girds.First,a dynamic physical grid model under FDIAs is modeled,in which model uncertainty and parameter uncertainty are taken into account.Then,an interval observer-based detection method against FDIAs is proposed,where a detection criteria using interval residual is put forward.Corresponding to the detection results,the resilient defense controller is triggered to defense the FDIAs if the system states are affected by FDIAs.Linear matrix inequality(LMI)approach is applied to design the resilient controller with H_(∞)performance.The system with the resilient defense controller can be robust to FDIAs and the gain of the resilient controller has a certain gain margin.Our active resilient defense approach can be built in real time and show accurate and quick respond to the injected FDIAs.The effectiveness of the proposed defense scheme is verified by the simulation results on an IEEE 30-bus grid system.
基金supported in part by the National Key Research and Development Program of China(No.2017YFB0902900)the State Grid Corporation of China
文摘Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system state estimation(DSSE).The uncertainty of the distribution network is represented by the interval of each state variable.A three-phase interval DSSE model is proposed to construct the interval of each state variable.An improved iterative algorithm(IIA)is developed to solve the interval DSSE model and to obtain the lower and upper bounds of the interval.A cyber-attack is detected when the value of the state variable estimated by the traditional DSSE is out of the corresponding interval determined by the interval DSSE.To validate the proposed cyber-attack detection strategy,the basic principle of the cyber-attack is studied,and its general model is formulated.The proposed cyber-attack model and detection strategy are conducted on the IEEE 33-bus and 123-bus systems.Comparative experiments of the proposed IIA,Monte Carlo simulation algorithm,and interval Gauss elimination algorithm prove the validation of the proposed method.
文摘Detecting cyber-attacks undoubtedly has become a big data problem. This paper presents a tutorial on data mining based cyber-attack detection. First,a data driven defence framework is presented in terms of cyber security situational awareness. Then, the process of data mining based cyber-attack detection is discussed. Next,a multi-loop learning architecture is presented for data mining based cyber-attack detection. Finally,common data mining techniques for cyber-attack detection are discussed.
基金supported by the National Natural Science Foundation of China(72171059,71771041,72121001)the Fundamental Research Funds for the Central Universities(FRFCU5710000220)the Natural Science Foundation of Heilongjiang Province,China(No.YQ2020G003).
文摘With the high-speed development of decentralized applications,account-based blockchain platforms have become a hotbed of various financial scams and hacks due to their anonymity and high financial value.Financial security has become a top priority with the sustainable development of blockchain-based platforms because of an increasing number of cyber attacks,which have resulted in a huge loss of crypto assets in recent years.Therefore,it is imperative to study the real-time detection of cyber attacks to facilitate effective supervision and regulation.To this end,this paper proposes the weighted and extended isolation forest algorithms and designs a novel framework for the real-time detection of cyber-attack transactions by thoroughly studying and summarizing real-world examples.Furthermore,this study develops a new detection approach for locating the compromised address of a cyber attack to resolve the data scarcity of hack addresses and reduce time consumption.Moreover,three experiments are carried out not only to apply on different types of cyber attacks but also to compare the proposed approach with the widely used existing methods.The results demonstrate the high efficiency and generality of the proposed approach.Finally,the lower time consumption and robustness of our method were validated through additional experiments.In conclusion,the proposed blockchain-oriented approach in this study can handle real-time detection of cyber attacks and has significant scope for applications.
基金Funding was provided by State Key Laboratory of Automotive Safety and Energy(Grant No.KF2021)SUG-NAP Grant of Nanyang Technological University,Singapore(Grant No.M4082268.050).
文摘As intelligent vehicles become increasingly computerized and networked,they gain more autonomous capabilities.However,they are also becoming more exposed to cyber-threats which are likely to be a more prominent concern.This paper proposes a cyber-attack detection method for autonomous vehicles based on secure estimation of vehicle states,with an example application under attacks in the vehicle localization system.To investigate the effects of vehicle model and estimator on the attack detection performance,different nonlinear vehicle dynamic models and estimation approaches are employed.The deviation between the measurement from the onboard sensors and the state estimation is monitored in real time.With the designed vehicle state estimator and preset threshold,the cyber-attack detection algorithm is further developed for autonomous vehicles,whose performance is tested in simulations where the vehicle localization system is assumed to be compromised during a double lane change maneuver.The test results demonstrate the feasibility and effectiveness of the proposed cyber-attack algorithm.In addition,the results illustrate the impacts of vehicle nonlinear characteristics on the cyber-attack detection performance.Beyond this,the effects of different vehicle models on the attack detection performance,as well as the selection of suitable filtering approaches for the attack detection,are also discussed.
基金Wang G and Giannakis GB were supported by the National Natural Science Foundation of China(NSFC)(Nos.1514056,1505970,and 1711471)Chen J and Sun J were supported by the NSFC(Nos.61621063 and 61522303)+2 种基金the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(No.61720106011)the Projects of Major International(Regional)Joint Research Program NSFC(No.61720106011)the Program for Changjiang Scholars and Innovative Research Team in University(No.IRT1208)
文摘In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of(industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate,fast, and robust power system state estimation(PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states(i.e., nodal voltage phasors).Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramér-Rao lower bound is developed.Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.
