Detection and Isolation of False Data Injection Attacks in Smart Grids via Nonlinear Interval Observer

Wang, Xinyu; Luo, Xiaoyuan; Zhang, Yuyan; Guan, Xinping

doi:10.1109/jiot.2019.2916670

Cited by 54 publications

(16 citation statements)

References 39 publications

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“…System state was estimated via observers, and a residual‐based criterion was then designed to detect and isolate potential false data injections. [36] also applied interval observer as the main approach, detecting false data injection attacks for large‐scale smart grids with an adaptive threshold. In [37], sources of false data injection attacks were modeled as unknown malicious sensors, and an optimal filter based detector was designed against potential attacks.…”

Section: Recent Advancesmentioning

confidence: 99%

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: Recent Advancesmentioning

confidence: 99%

Model‐based fault diagnosis of networked systems: A survey

Song

2021

Asian Journal of Control

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“…To solve the problem, they have been utilizing methods of reducing nuclear requirements and low-grade matrix factorization. In either scenario, the FDI attacks detection techniques for a broad electricity grid, suggested with the well-developed multifaceted complexity of computational technology [26][27]. Instead of the state estimates which might attain efficient FDI attack locations in four individual situations [28], a distributed host scheme was suggested based on collaborative determinations: single, sparse, random, and dense four kinds of appropriations for false measurement data.…”

Section: Related Workmentioning

confidence: 99%

Multi Label Deep Learning classification approach for False Data Injection Attacks in Smart Grid

2021

KSII TIIS

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The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14,30, 57, and

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“…Experimental results demonstrated that all of indications could successfully detect false data injection attack on smart grid. Wang et al [30] proposed a new detection method based on an interval observer against the false data injection attacks for smart grid. On basis of the work, Wang et al [31] designed a distributed detection and isolation method against false data injection attack by using a non-linear unknown input observer.…”

Section: Data Attack For Smart Gridmentioning

confidence: 99%

“…Most of the existing work focuses on the detection and countermeasures of false data injection attacks [24,27,[29][30][31][32] and abnormal data attack [25,26,28] on the power systems. However, these works do not take into account the security protection of data in the transmission process of the grid communication network.…”

Section: Data Attack For Smart Gridmentioning

confidence: 99%

Data filter function incremental mining based on feature selection in an active distribution network

Deng

Cai

Zhang

et al. 2020

IET cyber-phys. syst.

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Compared with traditional distributed networks, the complex access environment, flexible access mode, massive access terminal, and data in an active distribution network will bring great security challenges to data transmission. The existing data security methods, such as access control and encryption, address the security of massive, high dimensional, and non-text data in the active distribution network. Therefore, feature selection algorithm based on rough set is first given to reduce the complexity of massive and high dimensional data. And then, based on feature selection, the authors propose a data filtering function model mining algorithm by using gene expression programming (DFFM-FSGEP). Finally, to solve the data filter function model mining of the incremental dataset, they also present an incremental mining algorithm of the filtering function model based on functional fitting (IMFFM-FF). Experimental results show that the proposed algorithm in this study can greatly reduce the complexity of experimental datasets to be processed, and compared with the other algorithms, DFFM-FSGEP has higher classification accuracy and sensitivity, and IMFFM-FF has higher classification speed and classification accuracy for incremental datasets.

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Detection and Isolation of False Data Injection Attacks in Smart Grids via Nonlinear Interval Observer

Cited by 54 publications

References 39 publications

Model‐based fault diagnosis of networked systems: A survey

Model‐based fault diagnosis of networked systems: A survey

Multi Label Deep Learning classification approach for False Data Injection Attacks in Smart Grid

Data filter function incremental mining based on feature selection in an active distribution network

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