Revealing a New Vulnerability of Distributed State Estimation: A Data Integrity Attack and an Unsupervised Detection Algorithm

Shefaei, Alireza; Mohammadpourfard, Mostafa; Mohammadi‐Ivatloo, Behnam; Weng, Yang

doi:10.1109/tcns.2021.3091631

Cited by 16 publications

(2 citation statements)

References 41 publications

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“…Literature [19] introduces a network anomaly identification model based on bidirectional recurrent neural as the underlying logic, and performance tests based on a database of malicious attacks on power systems confirm that the model can fulfill the role of network anomaly identification. Literature [20] describes the characteristics of false data injection attacks based on distributed principles and conceives a network injection attack identification strategy based on unsupervised machine learning theory to detect and defend against the damage brought to network systems by false data injection attacks. Literature [21] designed a changeable network attack system combined with a local network information base for a deeper understanding of the principles that characterize network attacks, which helps to improve the knowledge of malicious attacks on the network and formulate corresponding measures to prevent them, which is of great significance to enhance the network security and stability of the power system.…”

Section: Introductionmentioning

confidence: 99%

Research on Attack Path Discovery of Power Information Network Based on Bayesian Inference

Li,

Liu,

Yao

et al. 2024

Applied Mathematics and Nonlinear Sciences

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This paper analyzes and researches the network attack in the electric power information environment. The intrusion attack steps are examined, and the Bayesian inference method is applied to investigate the attack source information network delivery. The success probability of the network attack is quantified by likelihood. Noisy Net, Dueling DQN, Soft Q-learning, Prioritized Experience Playback Mechanism, and ICM model are integrated to improve the DQN algorithm from different perspectives. A NDSPI-DQN algorithm is proposed based on Bayesian inference. The experimental results show that comparing the convergence performance of DQN, PPO, and this paper’s algorithm, both this paper’s algorithm and the PPO algorithm can converge to the maximum cumulative reward value within 1000 rounds, and this paper’s algorithm can converge to the optimal value within 350 rounds. In an environment with 120 hosts, the optimal path discovery success rate of this paper’s algorithm is 97.23%. The optimal number of iterations and average running time are 1.12 times and 3.81 seconds. The proposed method is suitable for large-scale power information networks with higher execution efficiency.

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Section: Introductionmentioning

confidence: 99%

Research on Attack Path Discovery of Power Information Network Based on Bayesian Inference

Li,

Liu,

Yao

et al. 2024

Applied Mathematics and Nonlinear Sciences

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“…The envision for smart grid systems is the bidirectional flow of energy and data between power suppliers and customers, resulting in a more efficient, stable, and automated energy network. However, since smart grids use a variety of information and communication technologies to accomplish this goal, they are more susceptible to cyber-attacks associated with significant political, financial, and physical damages [1], [2], [3]. Among the recognized cyber-attacks, attacks on the state estimation (SE) have been widely investigated [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Attack Detection and Localization in Smart Grid with Image-based Deep Learning

Mohammadpourfard¹,

Genç²,

Lakshminarayana³

et al. 2021

Preprint

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Smart grid's objective is to enable electricity and information to flow two-way while providing effective, robust, computerized, and decentralized energy delivery. This necessitates the use of state estimation-based techniques and real-time analysis to ensure that effective controls are deployed properly. However, the reliance on communication technologies makes such systems susceptible to sophisticated data integrity attacks imposing serious threats to the overall reliability of smart grid. To detect such attacks, advanced and efficient anomaly detection solutions are needed. In this paper, a two-stage deep learningbased framework is carefully designed by embedding power system's characteristics enabling precise attack detection and localization. First, we encode temporal correlations of the multivariate power system time-series measurements as 2D images using image-based representation approaches such as Gramian Angular Field (GAF) and Recurrence Plot (RP) to obtain the latent data characteristics. These images are then utilized to build a highly reliable and resilient deep Convolutional Neural Network (CNN)-based multi-label classifier capable of learning both low and high level characteristics in the images to detect and discover the exact attack locations without leveraging any prior statistical assumptions. The proposed method is evaluated on the IEEE 57-bus system using real-world load data. Also, a comparative study is carried out. Numerical results indicate that the proposed multi-class cyber-intrusion detection framework outperforms the current conventional and deep learning-based attack detection methods.

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An unsupervised false data detection method based on graph autoencoder and attention network in power grid

Yang,

Cai,

Liu

et al. 2024

Electr Eng

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Revealing a New Vulnerability of Distributed State Estimation: A Data Integrity Attack and an Unsupervised Detection Algorithm

Cited by 16 publications

References 41 publications

Research on Attack Path Discovery of Power Information Network Based on Bayesian Inference

Research on Attack Path Discovery of Power Information Network Based on Bayesian Inference

Attack Detection and Localization in Smart Grid with Image-based Deep Learning

An unsupervised false data detection method based on graph autoencoder and attention network in power grid

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