Detecting False Data Injection Attacks Against Power System State Estimation With Fast Go-Decomposition Approach

Li, Boda; Ding, Tao; Huang, Can; Zhao, Junbo; Yang, Yongheng; Chen, Ying

doi:10.1109/tii.2018.2875529

Cited by 97 publications

(21 citation statements)

References 29 publications

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“…Based on the decentralized nature of the proposed method, privacy among utilities could be increased and this led to a reduction in the size of the problem, which greatly decreased computational burden. In [19], it was shown that the FDI attack detection problem can be formed as a matrix separation problem due to the nature of the attack matrix, which is a sparse matrix, and the measurement matrix, which is a low-rank matrix. The present matrix separation approaches, such as the Augmented Lagrangian Method (ALM), Low-Rank Matrix Factorization (LRMF), and Double-Noise-Dual-Problem (DNDP)-ALM, suffer from higher computational time and lower accuracy.…”

Section: State Estimation Methodsmentioning

confidence: 99%

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

Mohammadi

2021

Energies

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In this paper, a brief survey of measurable factors affecting the adoption of cybersecurity enhancement methods in the smart grid is provided. From a practical point of view, it is a key point to determine to what degree the cyber resilience of power systems can be improved using cost-effective resilience enhancement methods. Numerous attempts have been made to the vital resilience of the smart grid against cyber-attacks. The recently proposed cybersecurity methods are considered in this paper, and their accuracies, computational time, and robustness against external factors in detecting and identifying False Data Injection (FDI) attacks are evaluated. There is no all-inclusive solution to fit all power systems requirements. Therefore, the recently proposed cyber-attack detection and identification methods are quantitatively compared and discussed.

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Section: State Estimation Methodsmentioning

confidence: 99%

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

Mohammadi

2021

Energies

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“…The authors in [21][22] suggested both accurate and approximate calculations, using the graphical strategies, to choose the basic number estimates besides system protection of many FDI attacks by state factors. For the next sort, the establishment of the power structure is important to the attacker's earlier knowledge.…”

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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“…To detect the attacks, using the Kalman filters, the system states are predicted and using generalized likelihood ratio approach the attack parameters are estimated. Considering low rank matrix of the measurement and sparse Jacobian of the electricity system, three different detection methods of FDI are compared in [13]. In this reference, augmented Lagrangian multipliers (ALM) and low rank matrix factorization method as bench mark algorithms to detect noisy measurements, are compared with Go decomposition method.…”

Section: A Current State Of Artmentioning

confidence: 99%

Modelling Cyber Attacks on Electricity Market Using Mathematical Programming With Equilibrium Constraints

et al. 2019

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With the development of communication infrastructure in smart grids, cyber security reinforcement has become one of the most challenging issues for power system operators. In this paper, an attacker is considered a participant in the virtual bidding procedure in the day-ahead (DA) and realtime (RT) electricity markets to maximize its profit. The cyber attacker attempts to identify the optimal power system measurements to attack along with the false data injected into measurement devices. Towards the maximum profit, the attacker needs to specify the relation between manipulated meters, virtual power traded in the markets, and electricity prices. Meanwhile, to avoid being detected by the system operator, the attacker considers the physical power system constraints existing in the DA and RT markets. Then, a bilevel optimization model is presented which combines the real electricity market state variables with the attacker decision-variables. Using the mathematical problem with equilibrium constraints, the presented bilevel model is converted into a single level optimization problem and the optimal decision variables for the attacker are obtained. Finally, simulation results are provided to demonstrate the performance of the attacker, which also provides insights for security improvement. INDEX TERMS Cyber-security, electricity market, false data injection, mathematical programming with equilibrium constraints.

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Detecting False Data Injection Attacks Against Power System State Estimation With Fast Go-Decomposition Approach

Cited by 97 publications

References 29 publications

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

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

Modelling Cyber Attacks on Electricity Market Using Mathematical Programming With Equilibrium Constraints

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