Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method
With the deeper penetration of inverter-based resources in power systems, false data injection attacks (FDIA) are a growing cyber-security concern. They have the potential to disrupt the system’s stability like frequency stability, thereby leading to catastrophic failures. Therefore, an F...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10819399/ |
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| author | Abhijeet Sahu Truc Nguyen Kejun Chen Xiangyu Zhang Malik Hassanaly |
| author_facet | Abhijeet Sahu Truc Nguyen Kejun Chen Xiangyu Zhang Malik Hassanaly |
| author_sort | Abhijeet Sahu |
| collection | DOAJ |
| description | With the deeper penetration of inverter-based resources in power systems, false data injection attacks (FDIA) are a growing cyber-security concern. They have the potential to disrupt the system’s stability like frequency stability, thereby leading to catastrophic failures. Therefore, an FDIA detection method would be valuable to protect power systems. FDIAs typically induce a discrepancy between the desired and the effective behavior of the power system dynamics. A suitable detection method can leverage power dynamics predictions to identify whether such a discrepancy was induced by an FDIA. This work investigates the efficacy of temporal and spatio-temporal state prediction models, such as Long Short-Term Memory (LSTM) and a combination of Graph Neural Networks (GNN) with LSTM, for predicting frequency dynamics in the absence of an FDIA but with noisy measurements, and thereby identify FDIA events. For demonstration purposes, the IEEE 39 New England Kron-reduced model simulated with a swing equation is considered. It is shown that the proposed state prediction models can be used as a building block for developing an effective FDIA detection method that can maintain high detection accuracy across various attack and deployment settings. It also shows how the FDIA detection should be deployed to limit its exposure to detection inaccuracies and mitigate its computational burden. |
| format | Article |
| id | doaj-art-6dcd6ec391a347e0af6573913cd9b6f9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6dcd6ec391a347e0af6573913cd9b6f92025-01-24T00:01:19ZengIEEEIEEE Access2169-35362025-01-0113124111242610.1109/ACCESS.2024.352494210819399Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction MethodAbhijeet Sahu0https://orcid.org/0000-0002-7647-3758Truc Nguyen1https://orcid.org/0000-0002-5836-5884Kejun Chen2Xiangyu Zhang3https://orcid.org/0000-0003-4857-2318Malik Hassanaly4https://orcid.org/0000-0002-0425-9090Cyber Security Center, National Renewable Energy Laboratory, Golden, CO, USAComputational Science Center, National Renewable Energy Laboratory, Golden, CO, USAComputational Science Center, National Renewable Energy Laboratory, Golden, CO, USAComputational Science Center, National Renewable Energy Laboratory, Golden, CO, USAComputational Science Center, National Renewable Energy Laboratory, Golden, CO, USAWith the deeper penetration of inverter-based resources in power systems, false data injection attacks (FDIA) are a growing cyber-security concern. They have the potential to disrupt the system’s stability like frequency stability, thereby leading to catastrophic failures. Therefore, an FDIA detection method would be valuable to protect power systems. FDIAs typically induce a discrepancy between the desired and the effective behavior of the power system dynamics. A suitable detection method can leverage power dynamics predictions to identify whether such a discrepancy was induced by an FDIA. This work investigates the efficacy of temporal and spatio-temporal state prediction models, such as Long Short-Term Memory (LSTM) and a combination of Graph Neural Networks (GNN) with LSTM, for predicting frequency dynamics in the absence of an FDIA but with noisy measurements, and thereby identify FDIA events. For demonstration purposes, the IEEE 39 New England Kron-reduced model simulated with a swing equation is considered. It is shown that the proposed state prediction models can be used as a building block for developing an effective FDIA detection method that can maintain high detection accuracy across various attack and deployment settings. It also shows how the FDIA detection should be deployed to limit its exposure to detection inaccuracies and mitigate its computational burden.https://ieeexplore.ieee.org/document/10819399/False data injectiondynamic state predictionlong short term memorygraph neural networks |
| spellingShingle | Abhijeet Sahu Truc Nguyen Kejun Chen Xiangyu Zhang Malik Hassanaly Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method IEEE Access False data injection dynamic state prediction long short term memory graph neural networks |
| title | Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method |
| title_full | Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method |
| title_fullStr | Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method |
| title_full_unstemmed | Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method |
| title_short | Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method |
| title_sort | detection of false data injection attacks fdia on power dynamical systems with a state prediction method |
| topic | False data injection dynamic state prediction long short term memory graph neural networks |
| url | https://ieeexplore.ieee.org/document/10819399/ |
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