Abstract
Electric utilities are in the process of installing millions of smart meters around the world, to help improve their power delivery service. Although many of these meters come equipped with encrypted communications, they may potentially be vulnerable to cyber intrusion attempts. These attempts may be aimed at stealing electricity, or destabilizing the electricity market system. Therefore, there is a need for an additional layer of verification to detect these intrusion attempts. In this paper, we propose an anomaly detection method that uniquely combines Principal Component Analysis (PCA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to verify the integrity of the smart meter measurements. Anomalies are deviations from the normal electricity consumption behavior. This behavior is modeled using a large, open database of smart meter readings obtained from a real deployment. We provide quantitative arguments that describe design choices for this method and use false-data injections to quantitatively compare this method with another method described in related work.
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Acknowledgements
This material is based upon work supported by the Department of Energy under Award Number DE-OE0000097. The smart meter data used in this paper is accessed via the Irish Social Science Data Archive - www.ucd.ie/issda. The providers of this data, the Commission for Energy Regulation, bear no responsibility for the further analysis or interpretation of it. We thank Shweta Ramdas, Jeremy Jones and Tim Yardley for their support.
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Badrinath Krishna, V., Weaver, G.A., Sanders, W.H. (2015). PCA-Based Method for Detecting Integrity Attacks on Advanced Metering Infrastructure. In: Campos, J., Haverkort, B. (eds) Quantitative Evaluation of Systems. QEST 2015. Lecture Notes in Computer Science(), vol 9259. Springer, Cham. https://doi.org/10.1007/978-3-319-22264-6_5
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DOI: https://doi.org/10.1007/978-3-319-22264-6_5
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