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

The proposed AAE learning-based approach employs LSTM in the architecture of the autoencoder for capturing the temporal correlation of the grid measurements to reconstruct the input measurement samples and uses two critics to distinguish the generated samples from the real data points.

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Mehdi Jabbari Zideh, Mohammad Reza Khalghani, Sarika Khushalani Solanki (2024). Dataset: An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids. https://doi.org/10.57702/kuj8stw0

DOI retrieved: December 3, 2024

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Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2404.02923
Author Mehdi Jabbari Zideh
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Mohammad Reza Khalghani
Sarika Khushalani Solanki