TY - JOUR
T1 - Energy theft detection for AMI using principal component analysis based reconstructed data
AU - Singh, Sandeep Kumar
AU - Bose, Ranjan
AU - Joshi, Anupam
N1 - Publisher Copyright:
© 2019 Institution of Engineering and Technology. All rights reserved.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067944803&partnerID=8YFLogxK
U2 - 10.1049/iet-cps.2018.5050
DO - 10.1049/iet-cps.2018.5050
M3 - Article
AN - SCOPUS:85067944803
SN - 2398-3396
VL - 4
SP - 179
EP - 185
JO - IET Cyber-Physical Systems: Theory and Applications
JF - IET Cyber-Physical Systems: Theory and Applications
IS - 2
ER -