Training Strategies for Autoencoder-based Detection of False Data Injection Attacks

Training Strategies for Autoencoder-based Detection of False Data Injection Attacks

Wang, C. (TU Delft Intelligent Electrical Power Grids)
Pan, K. (TU Delft Intelligent Electrical Power Grids)
Tindemans, Simon H. (TU Delft Intelligent Electrical Power Grids)
Palensky, P. (TU Delft Intelligent Electrical Power Grids)

2020

The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has the ability to achieve satisfactory learning efficiency and detection accuracy.

Anomaly detection
autoencoder
false data injection attack
hyperparameter tuning

http://resolver.tudelft.nl/uuid:aacbe5dd-e779-4271-ae67-4b90af323692

https://doi.org/10.1109/ISGT-Europe47291.2020.9248894

IEEE

2021-12-22

978-1-7281-7101-2

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe): Proceedings

10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020, 2020-10-26 → 2020-10-28, Virtual/online event due to COVID-19, Delft, Netherlands

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Institutional Repository

conference paper

© 2020 C. Wang, K. Pan, Simon H. Tindemans, P. Palensky