An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids

arXiv:2404.02923v1 Announce Type: cross
Abstract: Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the …

abstract adversarial arxiv attacks autoencoder challenges cs.ai cs.cr cs.lg cs.sy cyber cyber attacks detection distributed distribution eess.sy energy intermittent nature power resources smart stochastic systems type uncertain unsupervised