Intrusion detection in smart grid measurement infrastructures based on principal component analysis

Elisabeth Drayer, Tirza Routtenberg

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

3 Scopus citations


The extensive measurement infrastructure of smart grids is a vulnerable target for cyber attacks aiming at compromising reliable power supply. Thus, the detection of intrusion into the system and the identification of manipulated and false data is a key security capability required for future power systems. In this paper, we apply principal component analysis (PCA), together with a subspace analysis, to detect the presence of such false data injection (FDI) attacks. A key requirement for this method is a database of historical grid states that is used to compute the PCA transformation matrix. Each new grid state is then transformed based on this matrix to calculate its uncorrelated principal components. The presence of FDI attacks leads to a significant increase in the contribution of principal components that span the residual subspace. By comparing this projection against a threshold, the presence of compromised measurements can be detected. This is demonstrated by several case study simulations.

Original languageEnglish
Title of host publication2019 IEEE Milan PowerTech, PowerTech 2019
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781538647226
StatePublished - 1 Jun 2019
Event2019 IEEE Milan PowerTech, PowerTech 2019 - Milan, Italy
Duration: 23 Jun 201927 Jun 2019

Publication series

Name2019 IEEE Milan PowerTech, PowerTech 2019


Conference2019 IEEE Milan PowerTech, PowerTech 2019

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Safety, Risk, Reliability and Quality


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