Detection of False Data Injection Attacks in Unobservable Power Systems by Laplacian Regularization

Lital Dabush, Tirza Routtenberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The modern electrical grid is a complex cyber-physical system, and thus is vulnerable to measurement losses and attacks. In this paper, we consider the problem of detecting false data injection (FDI) attacks and bad data in unobservable power systems. Classical bad-data detection methods usually assume observable systems and cannot detect stealth FDI attacks. We use the smoothness property of the system states (voltages) w.r.t. the admittance matrix, which is also the Laplacian of the graph representation of the grid. First, we present the Laplacian-based regularized state estimator, which does not require full observability of the network. Then, we derive the Laplacian-regularized generalized likelihood ratio test (LR-GLRT). We show that the LR-GLRT has a component of a soft high-pass graph filter applied to the state estimator. Numerical results on the IEEE 118-bus system demonstrate that the LR-GLRT outperforms other detection approaches and is robust to missing data.

Original languageEnglish
Title of host publication2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, SAM 2022
PublisherIEEE Computer Society
Pages415-419
Number of pages5
ISBN (Electronic)9781665406338
DOIs
StatePublished - 1 Jan 2022
Event12th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2022 - Trondheim, Norway
Duration: 20 Jun 202223 Jun 2022

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Volume2022-June
ISSN (Electronic)2151-870X

Conference

Conference12th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2022
Country/TerritoryNorway
CityTrondheim
Period20/06/2223/06/22

Keywords

  • attack detection
  • bad-data detection
  • false data injection (FDI) attacks
  • Graph signal processing (GSP)
  • power system state estimation (PSSE)

ASJC Scopus subject areas

  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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