A two-layer game theoretical attack-defense model for a false data injection attack against power systems

Wang, Qi; Tai, Wei; Tang, Yi; Ni, Ming; You, Shi

doi:10.1016/j.ijepes.2018.07.007

Cited by 76 publications

(26 citation statements)

References 25 publications

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“…In [42], the characteristics of FDI attacks were determined from the attacker's perspective to find the weaknesses of existing BDD methods. Then, from the defender's perspective, a two-layer defense model, including identification and protection schemes, was proposed.…”

Section: Other 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: Other Methodsmentioning

confidence: 99%

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

Mohammadi

2021

Energies

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“…In China, many scholars have also discussed and studied FDIAs. Wang et al (2019) used the measured value in the phasor measurement unit as the object of attack and defense in the power system and adopted the load reduction caused by the line fault to quantify the consequences of the attack to achieve effective defense against FDIAs [ 17 ]. Chen et al (2019) revealed the potential connection between data attacks and physical consequences in the smart grid and analyzed the ways in which attackers launched malicious data attacks; this attack mechanism integrated the construction of optimal data attacks and identification of key lines, which had greater security and probability of occurrence [ 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning

Wang

Liu

et al. 2020

PLoS ONE

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To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. First, through the utilization of the standard IEEE node system and the simulation of FDIAs under the condition of noncomplete topology information, the construction of the attack data set is completed, and the MatPower tool is applied to simulate and analyze the data set. Second, based on the isolated Forest (iForest) abnormal score data processing algorithm combined with the Local Linear Embedding (LLE) data dimensionality reduction method, an algorithm for data feature extraction is constructed. Finally, based on the combination of the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) network, an algorithm model for FDIAs detection is constructed. The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. In the iFores algorithm, the number of iTree and the number of samples affect the extraction of abnormal score data. When the number of iTree n is determined to be 100, and the corresponding number of samples w is determined to be 10, the algorithm has the best detection effect. The FDIAs detection algorithm model based on CNN-GRU shows good detection effects under high attack intensity, with an accuracy rate of more than 95%, and its performance is better than other traditional detection algorithms. In this study, the bad data detection model based on deep learning has an active role in the realization of the safe and stable operation of the smart grid.

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“…However, the strong coupling between cyber and physical operations also makes smart grid vulnerable to various malicious cyber attacks [3]. During data transmission process of power emergency control services, rapid response demands cause a lack of encryption and detection ability in smart measurement devices [4]. Successful cyber attacks may cause regional blackouts, significant financial losses, and even endangering of human lives.…”

Section: Introductionmentioning

confidence: 99%

A Deviation-Based Detection Method Against False Data Injection Attacks in Smart Grid

et al. 2021

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State estimation plays a vital role to ensure safe and reliable operations in smart grid. Intelligent attackers can carefully design a destructive and stealthy false data injection attack (FDIA) sequence such that commonly used weighted least squares estimator combined with residual-based detection method is vulnerable to the FDIA. To effectively defend against an FDIA, in this paper, we propose a robust deviationbased detection method, in which an additional Kalman filter is introduced while retaining the original weighted least squares estimator, so that there are two state estimators. Moreover, an exponential weighting function is also applied to the introduced Kalman filter in our proposed method. When an FDIA occurs, the estimation results of weighted least squares estimator depend only on meter measurements at each time slot, but there is an adjustment process of estimated states for the Kalman filter based on historical states' transitions. Meanwhile, based on the exponential weighting function, estimated measurements in the Kalman filter can be adaptively suppressed for different attack strengths of FDIAs, and then the difference of the results of these two estimators increases. Subsequently, FDIAs can be effectively detected by checking the deviation of estimated measurements about the two estimators with a detection threshold. Experimental results validate the effectiveness of the proposed detection method against FDIAs. The impact of different attack strengths and noise on detection performance is also evaluated and analyzed.

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A two-layer game theoretical attack-defense model for a false data injection attack against power systems

Cited by 76 publications

References 25 publications

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning

A Deviation-Based Detection Method Against False Data Injection Attacks in Smart Grid

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