An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids
Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and l...
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| Vydáno v: | Electric power systems research Ročník 232; s. 110407 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.07.2024
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| Témata: | |
| ISSN: | 0378-7796, 1873-2046 |
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| Abstract | Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations and data labels. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other data-driven methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids.
•An unsupervised neural network model combines the GANs and autoencoder for FDI attacks.•The proposed model utilizes the outputs of autoencoder and critic score for attack detection.•The proposed model is tested on unbalanced power distribution grids integrated with DERs.•Simulation results verify the model’s superior performance compared to other data-driven models. |
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| AbstractList | Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations and data labels. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other data-driven methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids.
•An unsupervised neural network model combines the GANs and autoencoder for FDI attacks.•The proposed model utilizes the outputs of autoencoder and critic score for attack detection.•The proposed model is tested on unbalanced power distribution grids integrated with DERs.•Simulation results verify the model’s superior performance compared to other data-driven models. |
| ArticleNumber | 110407 |
| Author | Khalghani, Mohammad Reza Zideh, Mehdi Jabbari Solanki, Sarika Khushalani |
| Author_xml | – sequence: 1 givenname: Mehdi Jabbari orcidid: 0000-0001-7297-4021 surname: Zideh fullname: Zideh, Mehdi Jabbari email: mehdijabbari@ieee.org organization: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, 26506, WV, USA – sequence: 2 givenname: Mohammad Reza surname: Khalghani fullname: Khalghani, Mohammad Reza email: khalghani@ieee.org organization: Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, 33805, FL, USA – sequence: 3 givenname: Sarika Khushalani surname: Solanki fullname: Solanki, Sarika Khushalani email: skhushalanisolanki@mail.wvu.edu organization: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, 26506, WV, USA |
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| Keywords | Generative adversarial networks False data injection attacks Power distribution grids Cyber attack detection Unsupervised data-driven method Adversarial autoencoder |
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