A multi-sensor energy theft detection framework for advanced metering infrastructures

Stephen McLaughlin, Brett Holbert, Ahmed Fawaz, Robin Berthier, Saman Zonouz

Research output: Contribution to journalArticle

91 Scopus citations


The advanced metering infrastructure (AMI) is a crucial component of the smart grid, replacing traditional analog devices with computerized smart meters. Smart meters have not only allowed for efficient management of many end-users, but also have made AMI an attractive target for remote exploits and local physical tampering with the end goal of stealing energy. While smart meters posses multiple sensors and data sources that can indicate energy theft, in practice, the individual methods exhibit many false positives. In this paper, we present AMIDS, an AMI intrusion detection system that uses information fusion to combine the sensors and consumption data from a smart meter to more accurately detect energy theft. AMIDS combines meter audit logs of physical and cyber events with consumption data to more accurately model and detect theft-related behavior. Our experimental results on normal and anomalous load profiles show that AMIDS can identify energy theft efforts with high accuracy. Furthermore, AMIDS correctly identified legitimate load profile changes that more elementary analyses classified as malicious.

Original languageEnglish (US)
Article number6547839
Pages (from-to)1319-1330
Number of pages12
JournalIEEE Journal on Selected Areas in Communications
Issue number7
StatePublished - Jul 22 2013
Externally publishedYes


All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


  • Power grid critical infrastructures
  • advanced metering infrastructures
  • intrusion alert correlation
  • intrusion and energy theft detection
  • multi-sensor inference and information fusion

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