Detection of False Data Injection Attacks in Cyber-Physical Power Systems: An Adaptive Adversarial Dual Autoencoder With Graph Representation Learning Approach

False data injection attacks (FDIAs) are an important network attack threatening the security of power systems to tamper with instruments and measurements. Conventional FDIAs detection approaches are limited to processing the high-dimensional non-Euclidean correlation of grid data. Inspired by the r...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 73; S. 1 - 11
Hauptverfasser:	Feng, Hantong, Han, Yinghua, Si, Fangyuan, Zhao, Qiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Adaptive systems Algorithms Cyber-physical power systems Cyber-physical systems Data models Deep learning false data injection attacks (FDIAs) Feature extraction generative adversarial network (GAN) Generative adversarial networks graph convolutional network Graph representations Graphical representations Machine learning Power measurement power system state estimation (PSSE) Power systems State estimation Training
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
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