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An improved reduction method based networked control against false data injection attacks and stochastic input delay

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APPLIED MATHEMATICS AND COMPUTATION 2020年 385卷 125421-125421页

作者： Wang, Yingchun Zheng, Yu Xie, Xiangpeng Yang, Jun Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China

This paper concerns on an improved resilient control for the networked control systems (NCSs) against false data injection (FDI) attacks. First, a time-varying input delay is divided into two different time intervals, which is described by a Bernoulli stochastic process, and the FDI attacks are also established in a stochastic process. Second, an improved reduction transformation method is developed to get an equivalent model. Then, a new Lyapunov functional stability approach is proposed such that an asymptotically stable criterion in the mean-square sense of the closed-loop systems is obtained in forms of linear matrix inequalities (LMIs). In the end, a simulation example verifies the feasibility and effective-ness of the proposed approach. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Networked control systems false data injection attacks Input delay Reduction method

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A Deviation-Based Detection Method Against false data injection attacks in Smart Grid

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IEEE ACCESS 2021年 9卷 15499-15509页

作者： Pei, Chao Xiao, Yang Liang, Wei Han, Xiaojia Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Key Lab Networked Control Syst Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110169 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China

State estimation plays a vital role to ensure safe and reliable operations in smart grid. Intelligent attackers can carefully design a destructive and stealthy false data injection attack (FDIA) sequence such that commonly used weighted least squares estimator combined with residual-based detection method is vulnerable to the FDIA. To effectively defend against an FDIA, in this paper, we propose a robust deviation-based detection method, in which an additional Kalman filter is introduced while retaining the original weighted least squares estimator, so that there are two state estimators. Moreover, an exponential weighting function is also applied to the introduced Kalman filter in our proposed method. When an FDIA occurs, the estimation results of weighted least squares estimator depend only on meter measurements at each time slot, but there is an adjustment process of estimated states for the Kalman filter based on historical states' transitions. Meanwhile, based on the exponential weighting function, estimated measurements in the Kalman filter can be adaptively suppressed for different attack strengths of FDIAs, and then the difference of the results of these two estimators increases. Subsequently, FDIAs can be effectively detected by checking the deviation of estimated measurements about the two estimators with a detection threshold. Experimental results validate the effectiveness of the proposed detection method against FDIAs. The impact of different attack strengths and noise on detection performance is also evaluated and analyzed.

关键词： State estimation false data injection attacks smart grid cyber security Kalman filter cyber physical system

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The data recovery strategy on machine learning against false data injection attacks in power cyber physical systems

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MEASUREMENT & CONTROL 2024年第5期58卷 632-642页

作者： Li, Qinxue Yang, Xiaofen Xie, Xuhuan Liu, Guiyun Guangzhou Maritime Univ Dept Elect Engn Guangzhou Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou Peoples R China South China Univ Technol Sch Mech & Automot Engn Wushan Campus Guangzhou 510641 Peoples R China

During the transmission of power measurement data through communication networks from remote terminal unit (RTU) to the state estimator in Supervisory Control and data Acquisition (SCADA), power cyber-physical systems (PCPSs) are more susceptible to cyber-attacks. To mitigate that threat, this paper is concerned with a new data recovery strategy on machine learning against false data injection attacks (FDIAs) in PCPSs. Firstly, in view of the limited resources (such as limited energy) of adversaries and system protections, a sparse target false data injection attack (FDIA) is constructed. Then, the FDIA detection problem is transformed into a tripartite separation problem, and the alternating direction method of multipliers on proximal exchange (ADMM-PE) is adopted to complete the intrusion detection of FDIAs. In addition, with the help of reliable mask information and real incomplete measurement data provided by the FDIA detection, a similar supervised generative adversarial imputation networks (GAIN) is proposed to complete the measurement data recovery after FDIAs. Specifically, the pseudo labels generated by data analysis methods such as k-means clustering and support vector machine (SVM) to improve the accuracy of measurement data recovery. Finally, the experimental results of PCPSs show the effectiveness and superiority of the proposed data recovery strategy against FDIAs.

关键词： Power cyber-physical systems false data injection attacks measurement data recovery intrusion detection machine learning

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Distributed secure estimation for cyber-physical systems with fading measurements and false data injection attacks

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2020年第14期51卷 2746-2766页

作者： Li, Li Guo, Jingjing Xia, Yuanqing Yang, Hongjiu Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Beijing Inst Technol Sch Automat Beijing Peoples R China Tianjin Univ Sch Elect & Informat Engn Key Lab Proc Measurement & Control Tianjin Peoples R China

This paper is concerned with the distributed secure estimation problem for a cyber-physical system (CPS) with fading measurements and false data injection (FDI) attacks, where the fading probability of measurements is assumed to be individual for each sensor, and the FDI attacks occur at the physical system layer to intensively modify the system's state. For improving security of the system, an attack detection mechanism is proposed based on an FDI attack observer constructed by minimising the upper bound of observation error covariance. Then a complete distributed state estimation algorithm is given to provide locally reliable estimation. A sufficient condition is provided to guarantee boundedness of the estimation error covariance. Finally, the effectiveness of the proposed method is illustrated by numerical simulation and practical example simulation.

关键词： Distributed secure estimation cyber-physical systems fading measurements false data injection attacks attack detection

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Detection and defense of time-varying formation for unmanned aerial vehicles against false data injection attacks and external disturbance

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第3期34卷 1714-1731页

作者： Wang, Zixuan Liu, Yajuan North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 100096 Peoples R China

This article investigates the detection and defense method of time-varying formation for unmanned aerial vehicles (UAVs) against external disturbance and false data injection attacks (FDIAs). First, to detection attacks, a distributed attack identification filter is constructed for each UAV. Second, two distributed extended state observers are designed to separate and estimate external disturbances and FDIAs. Third, the distributed time-varying formation tracking protocol and defense strategy for attacks is designed by utilizing the estimated information. The formation system is shown to asymptotically track the desired formation and trajectory and meanwhile reject the disturbance and attacks if the expected formation satisfies the feasible condition by stability analysis. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.

关键词： attack detection extended state observer false data injection attacks formation control multiagents system

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Generalized Graph Neural Network-Based Detection of false data injection attacks in Smart Grids

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第3期7卷 618-630页

作者： Takiddin, Abdulrahman Atat, Rachad Ismail, Muhammad Boyaci, Osman Davis, Katherine R. Serpedin, Erchin Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Qatar Dept Elect & Comp Engn Doha Qatar Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA

data injection attacks (FDIAs) pose a significant threat to smart power grids. Recent efforts have focused on developing machine learning (ML)-based defense strategies against such attacks. However, existing strategies offer limited detection performance since they (a) lack the capability of embedding the spatial aspects of the power system topology in the detection mechanism, (b) offer topology-specific detection that does not generalize well to practical systems with seasonal reconfigurations in their topology, or (c) offer detection based on only seen types of FDIAs present in the training set. Therefore, in this paper, we aim to develop a defense strategy that offers an improved generalization ability and detection performance against unseen attacks. Towards this objective, we propose a graph autoencoder (GAE)-based detection strategy that (a) captures spatio-temporal features of power systems, hence, offering improved detection performance, (b) is trained on comprehensive graphs reflecting various realizations of power system topologies, hence, offering better generalization abilities, and (c) works effectively against unseen FDIAs. The proposed detector is trained and tested on various topological configurations from 14, 39, and 118-bus systems offering detection rates (DRs) of 93.6%, 95.7%, and 99.1%, respectively, when tested against unseen FDIAs and unseen topologies. This presents an improvement of 11.5 - 30% compared to existing ML-based strategies.

关键词： Detectors Topology Stochastic processes Power systems Training Geometry data models Cyberattacks false data injection attacks graph autoencoder graph neural network generalized detection machine learning smart power grids

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Leader-Following Consensus Control of Unknown Nonlinear MASs Under false data injection attacks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4513-4524页

作者： Wang, Meirong Hu, Jianqiang Alsaedi, Ahmed Cao, Jinde Southeast Univ Sch Math Nanjing 210096 Peoples R China King Abdulaziz Univ Dept Math Nonlinear Anal & Appl Math NAAM Res Grp Jeddah 21589 Saudi Arabia

This paper studies the distributed leader-following consensus problem of unknown nonlinear multi-agent systems (MASs) under false data injection attacks (FDIAs), where the followers connected to the leader may receive the injected false data from the leader's communication channels. Due to the existence of FDIAs, the real and broken leader state value is not available to the followers and cannot be used by followers' controllers, thus an attack compensator based on the errors between the predictive value and the actual measured value is added to the controller to mitigate the adverse effects of attacks. Fuzzy logic systems (FLSs) and Neural Network (NN) techniques are applied to approximate the unknown nonlinear dynamic by estimating the weight matrix. The proposed controller combines attack compensation with unknown nonlinear function compensation, and finally obtains sufficient conditions for the MASs to be ultimately uniformly bounded (UUB). Two algorithms are presented for undirected and directed communication topologies respectively and the simulation results verify the feasibility of the proposed consensus algorithms.

关键词： Distributed control false data injection attacks fuzzy logic systems neural networks resilient consensus unknown nonlinear multi-agent systems Distributed control false data injection attacks fuzzy logic systems neural networks resilient consensus unknown nonlinear multi-agent systems

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Impact analysis of false data injection attacks on power system static security assessment

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Journal of Modern Power Systems and Clean Energy 2016年第3期4卷 496-505页

作者： Jiongcong CHEN Gaoqi LIANG Zexiang CAI Chunchao HU Yan XU Fengji LUO Junhua ZHAO School of Electric Power South China University of TechnologyGuangzhouChina Electric Power Research Institute of Guangdong Power Grid GuangzhouChina Centre for Intelligent Electricity Networks University of NewcastleNewcastleAustralia School of Electrical and Information Engineering University of SydneySydneyAustralia School of Civil Engineering University of SydneySydneyAustralia School of Science&Engineering Chinese University of Hong Kong(Shenzhen)GuangdongChina

Static security assessment(SSA) is an important procedure to ensure the static security of the power *** recently show that cyber-attacks might be a critical hazard to the secure and economic operations of the power system. In this paper, the influences of false data injection attack(FDIA) on the power system SSA are studied. FDIA is a major kind of cyber-attacks that can inject malicious data into meters, cause false state estimation results, and evade being detected by bad data detection. It is firstly shown that the SSA results could be manipulated by launching a successful FDIA, which can lead to incorrect or unnecessary corrective actions. Then,two kinds of targeted scenarios are proposed, i.e., fake secure signal attack and fake insecure signal attack. The former attack will deceive the system operator to believe that the system operates in a secure condition when it is actually not. The latter attack will deceive the system operator to make corrective actions, such as generator rescheduling, load shedding, etc. when it is unnecessary and costly. The implementation of the proposed analysis is validated with the IEEE-39 benchmark system.

关键词： Cyber physical power system Static security assessment false data injection attacks State estimation

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Complete stealthiness false data injection attacks against dynamic state estimation in cyber-physical systems

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INFORMATION SCIENCES 2022年 586卷 408-423页

作者： Yang, Guang-Yuan Li, Xiao-Jian Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

This paper is concerned with the design of complete stealthiness false data injection (FDI) attacks in cyber-physical systems with Kullback-Leiber divergence (KLD) detector. Different from the FDI attacks against KLD detector in the related researches, the FDI attacks with complete stealthiness can completely remove their influences on innovation. Especially, we present the sufficient and necessary design conditions for achieving the attacks. In addition, the realization method of data-driven attacks based on subspace identification is introduced when the attacker cannot acquire system parameters. And a DC motor system and an unmanned ground vehicle system are employed to verify the effectiveness of our main results. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Cyber-physical systems false data injection attacks Complete stealthiness Dynamic state estimation

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Resilient Consensus Control for Linear Multi-agent System Against the false data injection attacks

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第7期21卷 2112-2123页

作者： Wang, Meirong Hu, Jianqiang Cao, Jinde Southeast Univ Sch Math Nanjing 211189 Peoples R China Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

This paper is concerned with the resilient consensus control problem of the linear multi-agent system under the false data injection attacks, where each agent may receive the injected false data by attackers from its neighbours through communication links. Due to the existence of the false data, the desired consensus of multi-agent can not be reached by the general distributed consensus algorithms. By constructing an extended state observer to estimate the state and the injected false data towards each agent, a distributed resilient consensus control algorithm is designed to offset the negative effect of the attacks. Firstly, a sufficient condition is derived for the undirected multi-agent system under a bounded and decaying false data injection. Secondly, a sufficient and necessary condition is provided for the directed multi-agent system under the false data injection with decaying rate. Finally, the simulation results illustrate the effectiveness of the proposed consensus algorithms against the false data attacks.

关键词： Distributed control extended state observer false data injection attacks multi-agent system resilient consensus

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