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- W3131516318 abstract "False Data Injection (FDI) attacks against power system state estimation are a growing concern for operators. Previously, most works on FDI attacks have been performed under the assumption of the attacker having full knowledge of the underlying system without clear justification. In this paper, we develop a topology-learning-aided FDI attack that allows stealthy cyber-attacks against AC power system state estimation without prior knowledge of system information. The attack combines topology learning technique, based only on branch and bus power flows, and attacker-side pseudo-residual assessment to perform stealthy FDI attacks with high confidence. This paper, for the first time, demonstrates how quickly the attacker can develop full-knowledge of the grid topology and parameters and validates the full knowledge assumptions in the previous work." @default.
- W3131516318 created "2021-03-01" @default.
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- W3131516318 date "2021-07-26" @default.
- W3131516318 modified "2023-09-23" @default.
- W3131516318 title "Topology Learning Aided False Data Injection Attack without Prior Topology Information" @default.
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- W3131516318 doi "https://doi.org/10.1109/pesgm46819.2021.9638211" @default.
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