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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Bibliographic Details

Published in:	IEEE transactions on artificial intelligence Vol. 5; no. 3; pp. 1287 - 1301
Main Authors:	Takiddin, Abdulrahman, Ismail, Muhammad, Atat, Rachad, Davis, Katherine R., Serpedin, Erchin
Format:	Journal Article
Language:	English
Published:	IEEE 01.03.2024
Subjects:	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
Online Access:	Get full text
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