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

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Abstract

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.
Original languageEnglish
Title of host publication2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)
Subtitle of host publicationProceedings
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-7281-7100-5
ISBN (Print)978-1-7281-7101-2
DOIs
Publication statusPublished - 2020
Event10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020 - Virtual/online event due to COVID-19, Delft, Netherlands
Duration: 26 Oct 202028 Oct 2020
Conference number: 10
https://ieee-isgt-europe.org/

Conference

Conference10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020
Abbreviated titleISGT-Europe 2020
CountryNetherlands
CityDelft
Period26/10/2028/10/20
OtherVirtual/online event due to COVID-19
Internet address

Keywords

  • Anomaly detection
  • autoencoder
  • false data injection attack
  • hyperparameter tuning

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