ARIMA-Based Modeling and Validation of Consumption Readings in Power Grids

  • Varun Badrinath Krishna
  • Ravishankar K. Iyer
  • William H. Sanders
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9578)

Abstract

Smart meters are increasingly being deployed to measure electricity consumption of residential as well as non-residential consumers. The readings reported by these meters form a time series, which is stored at electric utility servers for billing purposes. Invalid readings may be reported because of malicious compromise of the smart meters themselves, or of the network infrastructure that supports their communications. Although many of these meters come equipped with encrypted communications, they may potentially be vulnerable to cyber intrusions. Therefore, there is a need for an additional layer of validation to detect these intrusion attempts. In this paper, we make three contributions. First, we show that the ARMA model proposed in the anomaly detection literature is unsuitable for electricity consumption as most consumers exhibit non-stationary consumption behavior. We use automated model fitting methods from the literature to show that first-order differencing of these non-stationary readings makes them weakly stationary. Thus, we propose the use of ARIMA forecasting methods for validating consumption readings. Second, we evaluate the effectiveness of ARIMA forecasting in the context of a specific attack model, where smart meter readings are modified to steal electricity. Third, we propose additional checks on mean and variance that can mitigate the total amount of electricity that can be stolen by an attacker by \(77.46\,\%\). Our evaluation is based on a real, open dataset of readings obtained from 450 consumer meters.

Keywords

Smart Meter Anomaly Attack Detection Auto Regressive Moving Average Integrated Electricity Theft Cyber-physical ARIMA ARMA Forecasting Critical Infrastructure Security Measurements 

Notes

Acknowledgments

This material is based upon work supported by the Department of Energy under Award Number DE-OE0000097 and the Siebel Energy Institute. The smart meter data used in this paper was accessed via the Irish Social Science Data Archive at www.ucd.ie/issda. The providers of the data, the Commission for Energy Regulation, bear no responsibility for the further analysis or interpretation of it. We thank Jenny Applequist, Jeremy Jones and Timothy Yardley for their support, and Prof. Douglas L. Jones for his feedback.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Varun Badrinath Krishna
    • 1
  • Ravishankar K. Iyer
    • 1
  • William H. Sanders
    • 1
  1. 1.Department of Electrical and Computer EngineeringAdvanced Digital Sciences Center, Information Trust Institute, University of Illinois at Urbana-ChampaignUrbanaUSA

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