Detection of False Data Injection Attacks in Smart Grids Based on Graph Signal Processing

Elisabeth Drayer, Tirza Routtenberg

Research output: Contribution to journalArticlepeer-review

57 Scopus citations

Abstract

The smart grid combines the classical power system with the information technology, leading to a cyber-physical system. In such an environment, the malicious injection of data has the potential to cause severe consequences. Classical residual-based methods for bad data detection are unable to detect well designed false data injection (FDI) attacks. Moreover, most of the works on FDI attack detection are based on the linearized DC model of the power system and fails to detect attacks based on the AC model. The aim of this paper is to address these problems by using the graph structure of the grid and the AC power flow model. We derive an attack detection method that is able to detect previously undetectable FDI attacks. This method is based on concepts originating from graph signal processing (GSP). The proposed detection scheme calculates the graph Fourier transform of an estimated grid state and filters the graph's high-frequency components. By comparing the maximum norm of this outcome with a threshold, we can detect the presence of FDI attacks. Case studies on the IEEE 14-bus system demonstrate that the proposed method is able to detect a wide range of previously undetectable attacks, both on angles and on magnitudes of the voltages.

Original languageEnglish
Article number8784391
Pages (from-to)1886-1896
Number of pages11
JournalIEEE Systems Journal
Volume14
Issue number2
DOIs
StatePublished - 1 Jun 2020

Keywords

  • Bad data detection
  • cyber-physical system
  • false data injection (FDI)
  • graph Fourier transform (GFT)
  • graph signal processing (GSP)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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