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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)

Abstract

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.

Keywords

AMI Fraud detection Energy theft Smart grid SVM 

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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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