Detection of False Data Injection Attacks Using the Autoencoder Approach

Detection of False Data Injection Attacks Using the Autoencoder Approach

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

2020

State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in ‘normal’ operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.

nomaly detection
autoencoder
false data in-jection attack
unbalanced training data
machine learning

http://resolver.tudelft.nl/uuid:ca209c17-5729-4685-8d09-522dbd9f1a77

https://doi.org/10.1109/PMAPS47429.2020.9183526

IEEE

2021-12-22

978-1-7281-2822-1

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

2020 International Conference on Probabilistic Methods Applied to Power Systems, 2020-08-18 → 2020-08-21, Belgium

Virtual/online event due to COVID-19 Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

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