Evaluating Detectors on Optimal Attack Vectors That Enable Electricity Theft and der Fraud

Varun Badrinath Krishna, Carl A. Gunter, William H. Sanders

Research output: Contribution to journalArticlepeer-review


Worldwide, utilities are losing billions of dollars annually because of electricity theft. The detection of electricity theft has been a topic of research for decades. In this paper, we extend our prior work in the context of advanced metering infrastructures, wherein smart meters are compromised and made to under-report consumption. To the best of our knowledge, this paper presents the first study of meter fraud in the context of distributed energy resources (DERs). With an increased penetration of DERs in modern power grids, and with the decline in electricity prices, we show that there is incentive for electricity generators to over-report generation. We quantify the economic impact of cyber-attacks (on meters) that are optimal in that they maximize fraud while circumventing detectors. In doing so, we use consumption data from Ireland, solar generation data from the U.S. and Australia, and wind generation data from France.

Original languageEnglish (US)
Article number8355252
Pages (from-to)790-805
Number of pages16
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number4
StatePublished - Aug 2018


  • Smart meter
  • anomaly detection
  • attack
  • distributed energy resource
  • machine learning
  • smart grid

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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