Toward Data Integrity Attacks Against Distributed Dynamic State Estimation in Smart Grid

An, Dou; Zhang, Feiye; Cui, Feifei; Yang, Qingyu

doi:10.1109/tase.2023.3236102

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…As a result, it is a critically important yet complicated topic to investigate the security issues of distributed multi-sensor systems. In most existing research, some results such as [21][22][23] primarily addressed the distributed estimation problem under malicious cyber attacks on communication links connecting sensors, and only considering a single type of attack. To our knowledge, however, few research have addressed the distributed consensus estimation problem under hybrid attacks that occur between estimators, where data transmitted over wireless networks between nodes may be tampered with by attackers.…”

Section: Introductionmentioning

confidence: 99%

Distributed Consensus Estimation for Networked Multi-Sensor Systems under Hybrid Attacks and Missing Measurements

Cheng,

Yang,

Yuan

et al. 2024

Sensors

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Cyber-security research on networked multi-sensor systems is crucial due to the vulnerability to various types of cyberattacks. For the development of effective defense measures, attention is required to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker’s perspective. This paper aims to tackle the problem of distributed consensus estimation for networked multi-sensor systems subject to hybrid attacks and missing measurements. To account for both random denial of service (DoS) attacks and false data injection (FDI) attacks, a hybrid attack model on the estimator-to-estimator communication channel is presented. The characteristics of missing measurements are defined by random variables that satisfy the Bernoulli distribution. Then a modified consensus-based distributed estimator, integrated with the characteristics of hybrid attacks and missing measurements, is presented. For reducing the computational complexity of the optimal distributed estimation method, a scalable suboptimal distributed consensus estimator is designed. Sufficient conditions are further provided for guaranteeing the stability of the proposed suboptimal distributed estimator. Finally, a simulation experiment on aircraft tracking is executed to validate the effectiveness and feasibility of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Distributed Consensus Estimation for Networked Multi-Sensor Systems under Hybrid Attacks and Missing Measurements

Cheng,

Yang,

Yuan

et al. 2024

Sensors

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“…Considering the limitations of WLS, an unrolled spatiotemporal graph convolutional network is proposed for DSSE [19]. The mentioned state estimation methods belong to the centralized PSSE framework, and they may fail to deal with large-scale state estimation problems because of large computational load and heavy communication burden [20,21]. Furthermore, it is often difficult to gather information of the entire power system especially when the privacy protection is considered [22].…”

Section: Introductionmentioning

confidence: 99%

A distributed multi-area power system state estimation method based on generalized loss function

Chen

Liu

et al. 2023

Meas. Sci. Technol.

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For power system state estimation, the measurement noise is usually assumed to follow the Gaussian distribution and the widely used estimator is the weighted least squares (WLS). However, the Gaussian distribution assumption is not always true and the performance of WLS becomes bad when the measurement noise is non-Gaussian. In this paper, a new distributed state estimation (SE) method is proposed for multi-area power systems. The proposed distributed method is based on the generalized loss function so that it can reduce the influence of non-Gaussian noise. Further, thanks to the matrix-splitting technology, the proposed distributed method can be implemented in a distributed way so that the computation time in each local area can be reduced. The simulation results carried out in the IEEE 30-bus and 118-bus systems verify the robustness and effectiveness of the proposed distributed SE method.

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Outlier‐resistant state estimation for complex networks with random false data injection attacks under encoding–decoding mechanism

Liu,

Hu,

Jia

et al. 2024

Adaptive Control & Signal

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SummaryThis article focuses on the outlier‐resistant state estimation problem for discrete time‐varying complex networks (TVCNs) affected by random false data injection attacks (FDIAs) under an encoding–decoding mechanism (EDM). From the perspective of information security, a uniform‐quantization‐based EDM is employed to encrypt the transmitted data. During the data transmission process, a set of independent random variables governed by Bernoulli distribution is introduced to characterize the occurrence of random FDIAs. For the purpose of alleviating the passive impact of potential measurement outliers, a saturation structure is adopted during the estimator design. The gain matrix is given by minimizing the upper bound of estimation error covariance. According to the stochastic analysis method, it is shown that the state estimation error is bounded exponentially in mean‐square sense by providing new sufficient condition. It should be noted that we make the first attempt to develop new outlier‐resistant state estimation method with performance evolution criterion in the time‐varying perspective for TVCNs with random FDIAs under EDM. Finally, a simulation example with comparative experiment is presented to illustrate the effectiveness of the newly presented outlier‐resistant estimation algorithm.

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Toward Data Integrity Attacks Against Distributed Dynamic State Estimation in Smart Grid

Cited by 8 publications

References 40 publications

Distributed Consensus Estimation for Networked Multi-Sensor Systems under Hybrid Attacks and Missing Measurements

Distributed Consensus Estimation for Networked Multi-Sensor Systems under Hybrid Attacks and Missing Measurements

A distributed multi-area power system state estimation method based on generalized loss function

Outlier‐resistant state estimation for complex networks with random false data injection attacks under encoding–decoding mechanism

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