Detection of false data injection attack using graph signal processing for the power grid

Raksha Ramakrishna, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper we revisit the problem of False Data Injection (FDI) attacks to electric power systems synchrophasors measurements through the lens of graph signal processing (GSP). First, we introduce a physics based model that supports the empirical evidence that Phasor Measurement Unit (PMU) data are low-pass graph signals. This insight is used to investigate how one can leverage such a structure to construct more effective bad data detection (BDD) algorithms that can detect FDI attack signatures through appropriate utilization of the features of the PMU graph-signal.

Original languageEnglish (US)
Title of host publicationGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728127231
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 - Ottawa, Canada
Duration: Nov 11 2019Nov 14 2019

Publication series

NameGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings

Conference

Conference7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019
CountryCanada
CityOttawa
Period11/11/1911/14/19

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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