Ensemble regression model-based anomaly detection for cyber-physical intrusion detection in smart grids

Anna Magdalena Kosek, Oliver Gehrke

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

The shift from centralised large production to distributed energy production has several consequences for current power system operation. The replacement of large power plants by growing numbers of distributed energy resources (DERs) increases the dependency of the power system on small scale, distributed production. Many of these DERs can be accessed and controlled remotely, posing a cybersecurity risk. This paper investigates an intrusion detection system which evaluates the DER operation in order to discover unauthorized control actions. The proposed anomaly detection method is based on an ensemble of non-linear artificial neural network DER models which detect and evaluate anomalies in DER operation. The proposed method is validated against measurement data which yields a precision of 0.947 and an accuracy of 0.976. This improves the precision and accuracy of a classic model-based anomaly detection by 75.7% and 9.2%, respectively.
Original languageEnglish
Title of host publicationProceedings of 2016 IEEE Electrical Power and Energy Conference
Number of pages7
PublisherIEEE
Publication date2016
Pages1-7
ISBN (Print)978-1-5090-1919-9
DOIs
Publication statusPublished - 2016
EventIEEE Electrical Power and Energy Conference 2016 - Ottawa, Canada
Duration: 12 Oct 201614 Oct 2016

Conference

ConferenceIEEE Electrical Power and Energy Conference 2016
CountryCanada
CityOttawa
Period12/10/201614/10/2016
Series2016 Ieee Electrical Power and Energy Conference (epec)

Keywords

  • Data models
  • Density estimation robust algorithm
  • Training
  • Correlation
  • Power systems
  • Analytical models
  • Intrusion detection
  • power system
  • Data-driven modelling
  • machine learning
  • cyber-physical security
  • model-based anomaly detection
  • ensemble regression

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