Detection and isolation of false data injection attack via adaptive Kalman filter bank

Luo, Xiaoyuan; Zhu, Minggao; Wang, Xinyu; Guan, Xinping

doi:10.1080/23307706.2022.2139299

Cited by 13 publications

(5 citation statements)

References 34 publications

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“…According to the work in [28], the τ is set to three times the size of the noise, which can then meet a false alarm rate of less than 5%. According to the work in [16], the detection process of chi-square detector is given as follows.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Of note, the method for dividing power grid areas can be seen in [34]. In addition, the calculation of extended model parameters can be seen in [28]. It is assumed that hacker can inject multiple FDI attacks, such as the first generator bus at t ∈ (100 s − 160 s), sixth generator bus at t = 260 s, and thirteenth generator bus at t = 380 s. By designing a bank of UIOs for three subareas, the corresponding change in the estimated state can be obtained, as shown in Figure 8.…”

Section: Case 2: Detection and Identification Of Multiple Fdi Attacks...mentioning

confidence: 99%

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A Novel Detection and Identification Mechanism for Malicious Injection Attacks in Power Systems

Zhang,

Wang,

Ban

et al. 2023

Symmetry

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The integration of advanced sensor technology and control technology has gradually improved the operational efficiency of traditional power systems. Due to the undetectability of these attacks using traditional chi-square detection techniques, the state estimation of power systems is vulnerable to cyber–physical attacks, For this reason, this paper presents a novel detection and identification framework for detecting malicious attacks in power systems from the perspective of cyber–physical symmetry. To consider the undetectability of cyber–physical attacks, a physical dynamics detection model using the unknown input observers (UIOs) and cosine similarity theorem is proposed. Through the design of UIO parameters, the influence of attacks on state estimation can be eliminated. A cosine similarity value-based detection criterion is proposed to replace the traditional detection threshold. To further cut down the effects caused by malicious attacks, an observer combination-based attack identification framework is established. Finally, simulations are given to demonstrate that the proposed security method can detect and identify the injected malicious attacks quickly and effectively.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Case 2: Detection and Identification Of Multiple Fdi Attacks...mentioning

confidence: 99%

A Novel Detection and Identification Mechanism for Malicious Injection Attacks in Power Systems

Zhang,

Wang,

Ban

et al. 2023

Symmetry

Self Cite

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“…Rawat and Bajracharya [3] introduced cosine similarity and online cumulative sum detector, respectively, which effectively overcame the defects in [2], and the detection effect and robustness were enhanced. The adaptive unscented Kalman algorithm was proposed in [4]. Based on the state variables, the central limit theorem is used to construct Gaussian variables and combined with a quartile to detect FDIA.…”

Section: Introductionmentioning

confidence: 99%

Detection of false data injection attacks in smart grid based on adaptive inhibition unscented Kalman filter

Zhang,

Gao,

Zhu

et al. 2024

J. Phys.: Conf. Ser.

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False data injection attacks (FDIAs) cause incorrect system states by tampering with measurements, seriously affecting the EMS’s control process. However, the well-designed FDIAs can bypass traditional bad data detection (BDD) mechanisms. Aiming at the challenge, we improve the unscented Kalman filter and combine AIUKF with weighted least squares (WLS) to detect FDIAs. Utilizing the different convergence rates of the two estimators, the cosine similarity is introduced for FDIA detection. Various test conditions in IEEE-14-bus are simulated to underline the capability of AIUKF in state estimation. The results indicate that the proposed detection approach is superior for detecting FDIAs.

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“…Generally, there are denial-of-service (DoS) attacks, [8][9][10][11] deception attacks 12,13 and false data injection (FDI) attacks. 12,14,15 As a kind of DoS attacks, jamming attacks 14 can disturb the operation of the system by replaying previously transmitted signals, or they can block the signal transmission, which make the system paralyzed and inoperable. Compared with DoS attacks, FDI attacks are carefully compiled by the attacker, which are difficult to be detected and have strong invisibility.…”

Section: Introductionmentioning

confidence: 99%

“…However, this reliance on networks also exposes CPSs to a range of network attacks. Generally, there are denial‐of‐service (DoS) attacks, 8‐11 deception attacks 12,13 and false data injection (FDI) attacks 12,14,15 . As a kind of DoS attacks, jamming attacks 14 can disturb the operation of the system by replaying previously transmitted signals, or they can block the signal transmission, which make the system paralyzed and inoperable.…”

Section: Introductionmentioning

confidence: 99%

Robust data‐driven iterative learning control for nonlinear cyber‐physical systems

Shi

Che

2023

Intl J Robust & Nonlinear

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This article mainly studies the problem of the robust security iterative learning control for nonlinear cyber‐physical systems, which suffer from external disturbances and denial‐of‐service (DoS) attacks. First, the nonlinear system can be transformed into an iterative linear data model, which is only used for the controller parameter design and the stability analysis without the physical meaning. Then, an extended state observer is introduced to estimate external disturbances along the iteration axis. At the same time, considering the influence of DoS attacks, an attack compensation mechanism is designed for DoS attacks along the iteration axis. In addition, an iterative‐varying penalty is designed to accelerate the convergence of the tracking error. Further, the mathematical induction is used to decouple the control input from the tracking error and the compression mapping principle is utilized to prove that the tracking error is ultimately bounded under the influence of disturbances and DoS attacks. Finally, the main results are verified by the motor simulation.

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Detection and isolation of false data injection attack via adaptive Kalman filter bank

Cited by 13 publications

References 34 publications

A Novel Detection and Identification Mechanism for Malicious Injection Attacks in Power Systems

A Novel Detection and Identification Mechanism for Malicious Injection Attacks in Power Systems

Detection of false data injection attacks in smart grid based on adaptive inhibition unscented Kalman filter

Robust data‐driven iterative learning control for nonlinear cyber‐physical systems

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