Abstract
Electricity theft is estimated to cost billions of dollars per year in many countries. To reduce electricity theft, electric utilities are leveraging data collected by the new Advanced Metering Infrastructure (AMI) and using data analytics to identify abnormal consumption trends and possible fraud. In this paper, we propose the first threat model for the use of data analytics in detecting electricity theft, and a new metric that leverages this threat model in order to evaluate and compare anomaly detectors. We use real data from an AMI system to validate our approach.
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References
EWMA Control Charts, http://itl.nist.gov/div898/handbook/pmc/section3/pmc324.html
forecast package for R, http://robjhyndman.com/software/forecast/
RapidMiner, http://rapid-i.com/
Antmann, P.: Reducing technical and non-technical losses in the power sector. Technical report, World Bank (July 2009)
Appel, A.: Security seals on voting machines: A case study. ACM Transactions on Information and Systems Security 14, 1–29 (2011)
Bandim, C., Alves Jr., J., Pinto Jr., A., Souza, F., Loureiro, M., Magalhaes, C., Galvez-Durand, F.: Identification of energy theft and tampered meters using a central observer meter: a mathematical approach. In: 2003 IEEE PES Transmission and Distribution Conference and Exposition, vol. 1, pp. 163–168. IEEE (2003)
Breunig, M., Kriegel, H.-P., Ng, R.T., Sander, J.: Lof: Identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104. ACM (2000)
Brodsky, B., Darkhovsky, B.: Non-Parametric Methods in Change-Point Problems. Kluwer Academic Publishers (1993)
Davis, M.: Smartgrid device security. adventures in a new medium (July 2009), http://www.blackhat.com/presentations/bh-usa-09/MDAVIS/BHUSA09-Davis-AMI-SLIDES.pdf
De Buda, E.: System for accurately detecting electricity theft. US Patent Application 12/351978 (January 2010)
Depuru, S., Wang, L., Devabhaktuni, V.: Support vector machine based data classification for detection of electricity theft. In: Power Systems Conference and Exposition (PSCE), 2011 IEEE/PES, pp. 1–8 (March 2011)
ECI Telecom. Fighting Electricity Theft with Advanced Metering Infrastructure (March 2011)
Geschickter, C.: The Emergence of Meter Data Management (MDM): A Smart Grid Information Strategy Report. GTM Research (2010)
Krebs, B.: FBI: smart meter hacks likely to spread (April 2012), http://krebsonsecurity.com/2012/04/fbi-smart-meter-hacks-likely-to-spread/
Lesser, A.: When big IT goes after big data on the smart grid (March 2012), http://gigaom.com/cleantech/when-big-it-goes-after-big-data-on-the-smart-grid-2/
McLaughlin, S., Podkuiko, D., McDaniel, P.: Energy Theft in the Advanced Metering Infrastructure. In: Rome, E., Bloomfield, R. (eds.) CRITIS 2009. LNCS, vol. 6027, pp. 176–187. Springer, Heidelberg (2010)
McLaughlin, S., Podkuiko, D., Miadzvezhanka, S., Delozier, A., McDaniel, P.: Multi-vendor penetration testing in the advanced metering infrastructure. In: Proceedings of the Annual Computer Security Applications Conference (ACSAC) (December 2010)
Nagi, J., Yap, K.S., Tiong, S.K., Ahmed, S.K., Mohamad, M.: Nontechnical loss detection for metered customers in power utility using support vector machines. IEEE Transactions on Power Delivery Systems 25(2), 1162–1171 (2010)
Nizar, A., Dong, Z.: Identification and detection of electricity customer behaviour irregularities. In: Power Systems Conference and Exposition (PSCE), pp. 1–10 (March 2009)
Peterson, D.: AppSecDC in review: Real-world backdoors on industrial devices (April 2012), http://www.digitalbond.com/2012/04/11/appsecdc-in-review/
Smart Grid Interoperability Panel, editor. NISTIR 7628. Guidelines for Smart Grid Cyber Security. NIST (August 2010)
Sommer, R., Paxson, V.: Outside the closed world: On using machine learning for network intrusion detection. In: IEEE Symposium on Security and Privacy (2010)
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Mashima, D., Cárdenas, A.A. (2012). Evaluating Electricity Theft Detectors in Smart Grid Networks. In: Balzarotti, D., Stolfo, S.J., Cova, M. (eds) Research in Attacks, Intrusions, and Defenses. RAID 2012. Lecture Notes in Computer Science, vol 7462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33338-5_11
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DOI: https://doi.org/10.1007/978-3-642-33338-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33337-8
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