A data-based detection method against false data injection attacks

Charalambos Konstantinou, Michail Maniatakos

Research output: Contribution to journalArticlepeer-review

Abstract

Editor's notes: CPSs are vulnerable to process-aware attacks that aim to disrupt the proper functioning or hamper performance/efficiency/stability/safety of the physical systems/processes of the CPSs. This article considers utilization of state estimators in smart grids for detection of false data injection attacks using data-driven anomaly detection. Based on a local outlier factor approach, it is shown that false data injection attacks can be reliably detected without requiring prior information on power system parameters or topology. Simulation studies on an IEEE 14-bus system show the efficacy of the approach. - Farshad Khorrami, New York University.

Original languageEnglish (US)
Article number8894484
Pages (from-to)67-74
Number of pages8
JournalIEEE Design and Test
Volume37
Issue number5
DOIs
StatePublished - Oct 2020

Keywords

  • Cybersecurity
  • dimensionality reduction
  • false data injection attacks
  • outlier detection
  • state estimation

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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