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

doi:doi:10.57702/kuj8stw0

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.

BibTex:

@dataset{Mehdi_Jabbari_Zideh_and_Mohammad_Reza_Khalghani_and_Sarika_Khushalani_Solanki_2024,
    abstract = {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.},
    author = {Mehdi Jabbari Zideh and Mohammad Reza Khalghani and Sarika Khushalani Solanki},
    doi = {10.57702/kuj8stw0},
    institution = {No Organization},
    keyword = {'adversarial autoencoder', 'cyber attack detection', 'power distribution grids', 'unsupervised learning'},
    month = {dec},
    publisher = {TIB},
    title = {An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids},
    url = {https://service.tib.eu/ldmservice/dataset/an-unsupervised-adversarial-autoencoder-for-cyber-attack-detection-in-power-distribution-grids},
    year = {2024}
}