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      Energy theft detection for AMI using principal component analysis based reconstructed data

      , 1 , 2 , 3

      IET Cyber-Physical Systems: Theory & Applications

      The Institution of Engineering and Technology

      power system security, power engineering computing, principal component analysis, smart power grids, smart meters, security of data, entropy, probability, energy theft detection, reconstructed data, energy theft attacks, advanced metering infrastructure, principal component analysis approximation, PCA approximation, dimensionality reduction, high dimensional AMI data, daily basis, weekly basis, principal components, probability distribution, reconstructed consumption dataset, energy consumption, historical consumption, larger relative entropy, real-smart-meter data, high detection percentage, consumption trends, relative entropy

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          Abstract

          To detect energy theft attacks in advanced metering infrastructure (AMI), we propose a detection method based on principal component analysis (PCA) approximation. PCA approximation is introduced by dimensionality reduction of high dimensional AMI data and the authors extract the underlying consumption trends of a consumer that repeat on a daily or weekly basis. AMI data is reconstructed using principal components and used for computing relative entropy. In the proposed method, relative entropy is used to measure the similarity between two probability distributions derived from reconstructed consumption dataset. When energy theft attacks are injected into AMI, the probability distribution of energy consumption will deviate from the historical consumption, so leading to a larger relative entropy. The proposed detection method is tested under different attack scenarios using real-smart-meter data. Test results show that the proposed method can detect theft attacks with high detection percentage.

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          Most cited references 16

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          The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.
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            Security and Privacy Challenges in the Smart Grid

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                Author and article information

                Contributors
                Journal
                IET-CPS
                IET Cyber-Physical Systems: Theory & Applications
                IET Cyber-Phys. Syst., Theory Appl.
                The Institution of Engineering and Technology
                2398-3396
                2398-3396
                13 December 2018
                6 February 2019
                June 2019
                : 4
                : 2
                : 179-185
                Affiliations
                [1 ] Department of Electronics and Communication Engineering, Indian Institute of Information Technology Pune , Pune 412109, India
                [2 ] Department of Electrical Engineering, Indian Institute of Technology Delhi , New Delhi 110 016, India
                [3 ] Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County , Baltimore MD 21250, USA
                Article
                IET-CPS.2018.5050 CPS.2018.5050.R1
                10.1049/iet-cps.2018.5050

                This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License ( http://creativecommons.org/licenses/by-nc/3.0/)

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                Pages: 0
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                Research Article

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