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.
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Zanetti, M., Jamhour, E., Pellenz, M., Penna, M. (2016). A New SVM-Based Fraud Detection Model for AMI. In: Skavhaug, A., Guiochet, J., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2016. Lecture Notes in Computer Science(), vol 9922. Springer, Cham. https://doi.org/10.1007/978-3-319-45477-1_18
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DOI: https://doi.org/10.1007/978-3-319-45477-1_18
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