Detection and Isolation of False Data Injection Attacks in Smart Grid via Unknown Input Interval Observer

Wang, Xinyu; Luo, Xiaoyuan; Zhang, Mingyue; Jiang, Zhong‐Ping; Guan, Xinping

doi:10.1109/jiot.2020.2966221

Cited by 43 publications

(15 citation statements)

References 44 publications

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“…H∞ filtering and LMI Fading measurements [11,23] Quantization effects [74] Stochastic additive faults [15,28] Medium access constraints [14] Unknown transition probability [75] Comprehensive incomplete measurements [12,13,25,26] EM algorithm under the Bayesian framework Asynchronous measurements in distributed systems [54] Residual generating based on fault diagnosis filters and observers Additive faults & incomplete measurements [7,8,17,24,27,31,32,46,47] Attacks on sensors [73] Soft faults & packet dropouts [48] Actuator faults [42,45] Faulty periodic communication [30] Cyber attacks [39,76] Sliding mode observer Attacks on sensors [72] Unknown input observer False data injection attacks [35] Minimum-variance filtering and Kalman filtering Cyber attacks [20,37,64,70] Additive faults [49] Homomorphic encryption [77] Particle filtering Cyber attacks [41,69] Strong tracking filtering Packet dropouts [78] Distributed resilient filtering Sensor degradation [79] Self-learning approaches Additive sensor fault [71,75,…”

Section: Methodologies Major Problems Addressed Literaturementioning

confidence: 99%

“…Commonly applied approaches mainly focus on detecting attacks and improving system robustness against attacks. Structural vulnerability was modeled as an unknown input signal into the system [35]. System state was estimated via observers, and a residual-based criterion was then designed to detect and isolate potential false data injections.…”

Section: Considering the Problems Brought By Data Transmissionmentioning

confidence: 99%

See 1 more Smart Citation

Model‐based fault diagnosis of networked systems: A survey

Song

2021

Asian Journal of Control

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This paper reviews the latest development of fault diagnosis techniques of networked systems. Considering various challenges raised by unreliable local sensors, signal digitization, data transmission, as well as distributed architecture, recent progress on model‐based fault diagnosis of networked systems are overviewed. We summarize the typical application examples and the typical treatment methods related to scientific problems for better understanding the framework of this area. Some promising future fault diagnosis techniques in various scenarios, such as distributed systems, with communication protocols, with cyber security issues, with intelligent algorithm challenges, as well as within active fault diagnosis framework are discussed.

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Section: Methodologies Major Problems Addressed Literaturementioning

confidence: 99%

Section: Considering the Problems Brought By Data Transmissionmentioning

confidence: 99%

Model‐based fault diagnosis of networked systems: A survey

Song

2021

Asian Journal of Control

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“…In this case, the transformation equation ( 60) is not convenient because the rank condition equation (4) (or equation (29)) does not hold. To be able to use the LMI design method presented for descriptor systems, we need to find a convenient transformation of the system equation ( 27) into the form equation (1) for which the rank condition equation ( 4) is fulfilled.…”

Section: Second Case: C M =mentioning

confidence: 99%

“…Indeed, unknown input observers are highly used in fault diagnosis, [21][22][23][24][25][26] and more recently in cyberattacks detection. [27][28][29][30] Several unknown input observers based methods have been recently exploited in secure and resilient estimation of cyber-physical systems. [31][32][33][34] More importantly, unknown input observers will play an important role in learning-based estimation methods due to the presence of a set of unknown parameters to be estimated.…”

Section: Introductionmentioning

confidence: 99%

Unknown input estimation algorithms for a class of LPV/nonlinear systems with application to wastewater treatment process

Chaouche

Zemouche

Ramdani

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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This paper addresses the problem of unknown input estimation for a class of nonlinear systems with mixed nonlinear terms, namely Linear Parameter Varying (LPV) parts and purely Lipschitz nonlinearities. Three new unknown input estimation algorithms are proposed, where each algorithm depends on the distribution of the unknown inputs in the system. These algorithms provide estimation of the maximum possible unknown inputs in a system, contrarily to the methods available in the literature, which consider only particular cases. Before introducing these estimation algorithms, a general LMI-based [Formula: see text] observer design methodology is provided, as a preliminary result, for a class of nonlinear descriptor systems with nonlinear outputs. To this end, a specific Lyapunov function is exploited to avoid derivatives of the disturbances. The proposed LMI conditions are less conservative than those existing in the literature. This is due to the specific Lyapunov function, the use of Young inequality in a judicious way, and the reformulation of the Lipschitz inequality. The proposed algorithms are applied to a wastewater treatment model to show their effectiveness and performances.

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“…While papers such as [12] offer a partial blind model that builds branch information. Other works in this field include [13], where a method of FDI detection using unknown input internal observer is proposed. While in [14], event-based triggers are used to enhance phasor measurement unit (PMU) based detection.…”

Section: Background a Fdi Attacksmentioning

confidence: 99%

Topology Learning Aided False Data Injection Attack without Prior Topology Information

Higgins

Zhang

et al. 2021

2021 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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False Data Injection (FDI) attacks against power system state estimation are a growing concern for operators. Previously, most works on FDI attacks have been performed under the assumption of the attacker having full knowledge of the underlying system without clear justification. In this paper, we develop a topology-learning-aided FDI attack that allows stealthy cyber-attacks against AC power system state estimation without prior knowledge of system information. The attack combines topology learning technique, based only on branch and bus power flows, and attacker-side pseudo-residual assessment to perform stealthy FDI attacks with high confidence. This paper, for the first time, demonstrates how quickly the attacker can develop full-knowledge of the grid topology and parameters and validates the full knowledge assumptions in the previous work.

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Detection and Isolation of False Data Injection Attacks in Smart Grid via Unknown Input Interval Observer

Cited by 43 publications

References 44 publications

Model‐based fault diagnosis of networked systems: A survey

Model‐based fault diagnosis of networked systems: A survey

Unknown input estimation algorithms for a class of LPV/nonlinear systems with application to wastewater treatment process

Topology Learning Aided False Data Injection Attack without Prior Topology Information

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