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• W4285151223 abstract "With the development of science and technology, traditional power systems are transforming to smart grids. At the same time as the transformation, many cyber-attacks also followed. The recent network attack is a false data injection attack (FDIA), which can use the learned power system configuration knowledge to launch an attack. Injecting false data into the state variables causes the state variables to produce an error state, which causes the data collection and monitoring system to make wrong decisions, which seriously threatens the safety of the power grid. In summary, this paper proposes a new detection method, which combines edge computing and federated learning. Split the system into multiple subsystems, and set edge node detectors in the subsystems to collect and detect data. Combined with deep learning, this method constructs CNN-LSTM model detector to extract the characteristics of data, and combined with federal learning method, trains the model locally to realize efficient and low delay FDIA detection." @default.
• W4285151223 created "2022-07-14" @default.
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• W4285151223 date "2022-01-01" @default.
• W4285151223 modified "2023-09-30" @default.
• W4285151223 title "Grid False Data Intrusion Detection Method Based on Edge Computing and Federated Learning" @default.
• W4285151223 cites W2005028112 @default.
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• W4285151223 doi "https://doi.org/10.1007/978-981-19-2448-4_17" @default.
• W4285151223 hasPublicationYear "2022" @default.
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