Detection of False Data Injection Attacks Using the Autoencoder Approach

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publication2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-2822-1
DOIs
Publication statusPublished - 18 Aug 2020
Event2020 International Conference on Probabilistic Methods Applied to Power Systems - , Belgium
Duration: 18 Aug 202021 Aug 2020

Conference

Conference2020 International Conference on Probabilistic Methods Applied to Power Systems
Abbreviated titlePMAPS 2020
CountryBelgium
Period18/08/2021/08/20
OtherVirtual/online event due to COVID-19

Keywords

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

Fingerprint Dive into the research topics of 'Detection of False Data Injection Attacks Using the Autoencoder Approach'. Together they form a unique fingerprint.

Cite this