A New SVM-Based Fraud Detection Model for AMI

  • Marcelo Zanetti
  • Edgard JamhourEmail author
  • Marcelo Pellenz
  • Manoel Penna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9922)


This paper presents a new strategy for fraud detection in Advanced Metering Infrastructure (AMI) based on the analysis of disturbances in the pattern consumption of end-customers. The proposed strategy is based on the use of SVM (Supported Vector Machine). SVM requires labeled training data in order to define a classification function. The need of labeled data is a serious limitation for practical implementation of fraud detection systems in AMI. To work around this problem, we propose a new strategy for training SVM classifiers that requires only normal consumption patterns in the training phase. The anomalous consumption is generated by simulating attacks on the normal consumption patterns.


AMI Fraud detection Energy theft Smart grid SVM 


  1. 1.
    Anas, M., Javaid, N., Mahmood, A., Raza, S.M., Qasim, U., Khan, Z.A.: Minimizing electricity theft using smart meters in AMI. In: 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pp. 176–182, November 2012Google Scholar
  2. 2.
    Angelos, E.W.S., Saavedra, O.R., Cortés, O.A.C., de Souza, A.N.: Detection and identification of abnormalities in customer consumptions in power distribution systems. IEEE Trans. Power Deliv. 26(4), 2436–2442 (2011)CrossRefGoogle Scholar
  3. 3.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  4. 4.
    Depuru, S.S.S.R., Wang, L., Devabhaktuni, V., Green, R.C.: High performance computing for detection of electricity theft. Int. J. Electr. Power Energy Syst. 47, 21–30 (2013)CrossRefGoogle Scholar
  5. 5.
    Depuru, S.S.S.R., Wang, L., Devabhaktuni, V.: Support vector machine based data classification for detection of electricity theft. In: 2011 IEEE/PES Power Systems Conference and Exposition (PSCE), pp. 1–8, March 2011Google Scholar
  6. 6.
    Jiang, R., Lu, R., Wang, Y., Luo, J., Shen, C., Shen, X.S.: Energy-theft detection issues for advanced metering infrastructure in smart grid. Tsinghua Sci. Technol. 19(2), 105–120 (2014)CrossRefGoogle Scholar
  7. 7.
    McLaughlin, S., Holbert, B., Fawaz, A., Berthier, R., Zonouz, S.: A multi-sensor energy theft detection framework for advanced metering infrastructures. IEEE J. Sel. Areas Commun. 31(7), 1319–1330 (2013)CrossRefGoogle Scholar
  8. 8.
    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 Trans. Power Deliv. 25(2), 1162–1171 (2010)CrossRefGoogle Scholar
  9. 9.
    NIST. Nist framework and roadmap for smart grid interoperatibility satandards, release 3. Technical report, U.S. Department of Commerce (2014)Google Scholar
  10. 10.
    Department of Industry and Science. Sample household electricity time of use data, July 2014Google Scholar
  11. 11.
    Salinas, S., Li, M., Li, P.: Privacy-preserving energy theft detection in smart grids. In: 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pp. 605–613, June 2012Google Scholar
  12. 12.
    Skopik, F., Ma, Z.: Attack vectors to metering data in smart grids under security constraints. In: 2012 IEEE 36th Annual Computer Software and Applications Conference Workshops (COMPSACW), pp. 134–139, July 2012Google Scholar
  13. 13.
    World Bank. Reducing technical and non-technical losses in the power sector. World Bank Group, Washington, DC (2009)Google Scholar
  14. 14.
    Xiao, Z., Xiao, Y., Du, D.H.: Exploring malicious meter inspection in neighborhood area smart grids. IEEE Trans. Smart Grid 4(1), 214–226 (2013)CrossRefGoogle Scholar
  15. 15.
    Xiao, Z., Xiao, Y., Du, D.H.-C.: Building accountable smart grids in neighborhood area networks. In: 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011), pp. 1–5, December 2011Google Scholar
  16. 16.
    Xiao, Z., Xiao, Y., Du, D.H.-C.: Non-repudiation in neighborhood area networks for smart grid. IEEE Commun. Mag. 51(1), 18–26 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Marcelo Zanetti
    • 1
  • Edgard Jamhour
    • 1
    Email author
  • Marcelo Pellenz
    • 1
  • Manoel Penna
    • 1
  1. 1.Pontifical Catholic University of ParanaCuritibaBrazil

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