Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids

Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an as...

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Vydáno v:	IEEE transactions on artificial intelligence Ročník 5; číslo 3; s. 1287 - 1301
Hlavní autoři:	Takiddin, Abdulrahman, Ismail, Muhammad, Atat, Rachad, Davis, Katherine R., Serpedin, Erchin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	IEEE 01.03.2024
Témata:	Adversarial samples Computer crime cybersecurity Data models data poisoning Detectors false data injection attacks (FDIA) graph autoencoder graph neural networks (GNNs) machine learning (ML) Power measurement Smart grids Topology Training
ISSN:	2691-4581, 2691-4581
On-line přístup:	Získat plný text
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