Robust data-driven adversarial false data injection attack detection method with deep Q-network in power systems

Robust data-driven adversarial false data injection attack detection method with deep Q-network in power systems

Electric power systems have been increasingly subjected to false data injection attacks (FDIAs) and adversarial examples, which inject well-designed disturbance signals into the measurements, and thereby generate erroneous state estimation (SE) results. The present work addresses this issue by propo...

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Bibliographic Details
Main Authors:	Ran, Xiaohong, Tay, Wee Peng, Lee, Christopher Ho Tin
Other Authors:	School of Electrical and Electronic Engineering
Format:	Journal Article
Language:	English
Published:	2024
Subjects:	Engineering Adversarial examples Deep reinforcement learning
Online Access:	https://hdl.handle.net/10356/176345

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