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Open Access

Energy-Theft Detection Issues for Advanced Metering Infrastructure in Smart Grid

School of Computer, National University of Defense Technology, Changsha 410073, China
School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore
Communication Engineering Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China
Computing Technology Institute of China Navy, Beijing 100841, China
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada
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Abstract

With the proliferation of smart grid research, the Advanced Metering Infrastructure (AMI) has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than $25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection.

Keywords

smart gridAdvanced Metering Infrastructure (AMI)securityenergy-theft detection

References

[1]
R. Jiang, R. Lu, C. Lai, J. Luo, and X. Shen, Robust group key management with revocation and collusion resistance for scada in smart grid, in Proc. IEEE Globe Communication Conference (Globecom), 2013, pp. 824-829.
Crossref
[2]
2012 India blackouts, http://en.wikipedia.org/wiki/Indiabla- ckout, 2013.
[3]
R. Lu, X. Liang, X. Li, X. Lin, and X. Shen, EPPA: An efficient and privacy-preserving aggregation scheme for secure smart grid communications, IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 9, pp. 1621-1631, 2012.
Crossref Google Scholar
[4]
M. Wen, R. Lu, J. Lei, H. Li, X. Liang, and X. Shen, SESA: An efficient searchable encryption scheme for auction in emerging smart grid marketing, Security and Communication Networks, vol. 7, no. 1, pp. 234-244, 2014.
Crossref Google Scholar
[5]
H. Li, X. Liang, R. Lu, X. Lin, H. Yang, and X. Shen, EPPDR: An efficient privacy-preserving demand response scheme with adaptive key evolution in smart grid, IEEE Transactions on Parallel and Distributed Systems, vol. PP, no. 99, pp. 1-10, 2013.
Google Scholar
[6]
X. Liang, X. Li, R. Lu, X. Lin, and X. Shen, UDP: Usage-based dynamic pricing with privacy preservation for smart grid, IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 141-150, 2013.
Crossref Google Scholar
[7]
Independent Electricity System Operator (IESO) Office of the Information and Privacy Commissioner Ontario, Canada, Building privacy into ontario’s smart meter data management system: A control framework, Tech. Rep., http://www.ipc.on.ca/english/Resources/Discussion-Papers/ Discussion-Papers-Summary/?id=1183, May 07, 2012.
[8]
IEE Report, Utility-scale smart meter deployments, plans, & proposals, http://www.edisonfoundation.net/iee/Docu- ments/IEE_SmartMeterRollouts_0512.pdf, 2012.
[9]
Z. M. Fadlullah, N. Kato, R. Lu, X. Shen, and Y. Nozaki, Toward secure targeted broadcast in smart grid, IEEE Communications Magazine, vol. 50, no. 5, pp. 150-156, 2012.
Crossref Google Scholar
[10]
M. M. Fouda, Z. M. Fadlullah, N. Kato, R. Lu, and X. Shen, A lightweight message authentication scheme for smart grid communications, IEEE Transactions on Smart Grid, vol. 2, no. 4, pp. 675-685, 2011.
Crossref Google Scholar
[11]
P. Antmann, Reducing technical and non-technical losses in the power sector, Background paper for the WBG Energy Strategy, Tech. Rep., Washington, DC, USA: The World Bank, 2009.
[12]
P. McDanieland S. McLaughlin, Security and privacy challenges in the smart grid, IEEE Security & Privacy, vol. 7, no. 3, pp. 75-77, 2009.
Crossref Google Scholar
[13]
B. Krebs, FBI: Smart meter hacks likely to spread, http://krebsonsecurity.com/2012/04/fbi-smart-meter-hacks- likely-to-spread/, 2012.
[14]
CBC News, Electricity theft by B.C. grow-ops costs $100m a year, http://www.cbc.ca/news/canada/british-columbia/electricity-theft-by-b-c-grow-ops-costs-100m-a-year-1.969837, 2010.
[15]
Ministry of power, India, Overview of power distribution, Tech. Rep., http://www.powermin.nic.in, 2013.
[16]
Federal Court of Audit, Operational audit report held in national agency of electrical energy, aneel, Brazil, Tech. Rep., No. TC 025.619/2007-2, 2007.
[17]
Electric meter hack! how to cut your electricity bill in half! http://www.youtube.com/watch?v=YVA8M2YV_ QW8, 2013.
[18]
S. McLaughlin, D. Podkuiko, and P. McDaniel, Energy theft in the advanced metering infrastructure, in Proc. the 4th International Conference on Critical Information Infrastructures Security, Springer, 2010, pp. 176-187.
Crossref
[19]
B. Schneier, Attack trees, Dr. Dobbs Journal, vol. 24, no. 12, pp. 21-29, 1999.
Google Scholar
[20]
R. Jiang, J. Luo, and X. Wang, An attack tree based risk assessment for location privacy in wireless sensor networks, in Proc. 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), 2012, pp. 1-4.
[21]
F. Skopik and Z. Ma, Attack vectors to metering data in smart grids under security constraints, in Proc. IEEE 36th Annual Computer Software and Applications Conference Workshops, 2012, pp. 134-139.
Crossref
[22]
A. Hahnand M. Govindarasu, Cyber attack exposure evaluation framework for the smart grid, IEEE Transactions on Smart Grid, vol. 2, no. 4, pp. 835-843, 2011.
Crossref Google Scholar
[23]
S. Depuru, L. Wang, and V. Devabhaktuni, Electricity theft: Overview, issues, prevention and a smart meter based approach to control theft, Energy Policy, vol. 39, no. 2, pp. 1007-1015, 2011.
Crossref Google Scholar
[24]
D. Grochocki, J. H. Huh, R. Berthier, R. Bobba, W. H. Sanders, A. A. Cárdenas, and J. G. Jetcheva, AMI threats, intrusion detection requirements and deployment recommendations, in Proc. 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), 2012, pp. 395-400.
[25]
R. J. Boltonand D. J. Hand, Statistical fraud detection: A review, Statistical Science, vol. 17, no. 3, pp. 235-249, 2002.
Crossref Google Scholar
[26]
Y. Kou, C.-T. Lu, S. Sirwongwattana, and Y.-P. Huang, Survey of fraud detection techniques, in Proc. IEEE International Conference on Networking, Sensing and Control, 2004, vol. 2, pp. 749-754.
[27]
J. Nagi, K. Yap, S. Tiong, S. Ahmed, and A. Mohammad, Detection of abnormalities and electricity theft using genetic support vector machines, in Proc. TENCON 2008-2008 IEEE Region 10 Conference, 2008, pp. 1-6.
[28]
J. Nagi, K. S. Yap, S. K. Tiong, S. K. Ahmed, and M. Mohamad, Nontechnical loss detection for metered customers in power utility using support vector machines, IEEE Transactions on Power Delivery, vol. 25, no. 2, pp. 1162-1171, 2010.
Crossref Google Scholar
[29]
S. Depuru, L. Wang, and V. Devabhaktuni, Support vector machine based data classification for detection of electricity theft, in Proc. 2011 IEEE/PES Power Systems Conference and Exposition (PSCE), 2011, pp. 1-8.
[30]
J. Nagi, K. S. Yap, S. K. Tiong, S. K. Ahmed, and F. Nagi, Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system, IEEE Transactions on Power Delivery, vol. 26, no. 2, pp. 1284-1285, 2011.
Crossref Google Scholar
[31]
S. Depuru, L. Wang, and V. Devabhaktuni, Enhanced encoding technique for identifying abnormal energy usage pattern, in Proc. IEEE North American Power Symposium (NAPS), 2012, pp. 1-6.
Crossref
[32]
S. Depuru, L. Wang, V. Devabhaktuni, and R. C. Green, High performance computing for detection of electricity theft, International Journal of Electrical Power & Energy Systems, vol. 47, pp. 21-30, 2013.
Crossref Google Scholar
[33]
E. W. S. dos Angelos, O. R. Saavedra, O. A. Cortes, and A. N. de Souza, Detection and identification of abnormalities in customer consumptions in power distribution systems, IEEE Transactions on Power Delivery, vol. 26, no. 4, pp. 2436-2442, 2011.
Crossref Google Scholar
[34]
C. Muniz, K. Figueiredo, M. Vellasco, G. Chavez, and M. Pacheco, Irregularity detection on low tension electric installations by neural network ensembles, in Proc. IEEE International Joint Conference on Neural Networks, 2009, pp. 2176-2182.
[35]
D. Mashima and A. A. Cárdenas, Evaluating electricity theft detectors in smart grid networks, in Research in Attacks, Intrusions, and Defenses, Springer, 2012, pp. 210-229.
Crossref
[36]
S. Salinas, M. Li, and P. Li, Privacy-preserving energy theft detection in smart grids, in Proc. 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2012, pp. 605-613.
Crossref
[37]
S. Salinas, M. Li, and P. Li, Privacy-preserving energy theft detection in smart grids: A P2P computing approach, IEEE Journal on Selected Areas in Communications, vol. 31, no. 9, pp. 257-267, 2013.
Crossref Google Scholar
[38]
S. McLaughlin, B. Holbert, S. Zonouz, and R. Berthier, AMIDS: A multi-sensor energy theft detection framework for advanced metering infrastructures, in Proc. IEEE Third International Conference on Smart Grid Communications (SmartGridComm), 2012, pp. 354-359.
[39]
S. McLaughlin, B. Holbert, A. Fawaz, R. Berthier, and S. Zonouz, A multi-sensor energy theft detection framework for advanced metering infrastructures, IEEE Journal on Selected Areas in Communications, vol. 31, no. 7, pp. 1319-1330, 2013.
Crossref Google Scholar
[40]
C.-H. Loand N. Ansari, CONSUMER: A novel hybrid intrusion detection system for distribution networks in smart grid, IEEE Transactions on Emerging Topics in Computing, vol. 1, no. 1, pp. 33-44, 2013.
Crossref Google Scholar
[41]
S. Depuru, L. Wang, V. Devabhaktuni, and N. Gudi, Measures and setbacks for controlling electricity theft, in Proc. IEEE North American Power Symposium (NAPS), 2010, pp. 1-8.
[42]
B. Khoo and Y. Cheng, Using RFID for anti-theft in a chinese electrical supply company: A cost-benefit analysis, in Proc. IEEE Wireless Telecommunications Symposium (WTS), 2011, pp. 1-6.
[43]
Z. Xiao, Y. Xiao, and D. H.-C. Du, Non-repudiation in neighborhood area networks for smart grid, IEEE Communications Magazine, vol. 51, no. 1, pp. 18-26, 2013.
Crossref Google Scholar
[44]
S.-C. Huang, Y.-L. Lo, and C.-N. Lu, Non-technical loss detection using state estimation and analysis of variance, IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 2959-2966, 2013.
Crossref Google Scholar
[45]
T. Liu, Y. Gu, D. Wang, Y. Gui, and X. Guan, A novel method to detect bad data injection attack in smart grid, in Proc. IEEE INFOCOM, 2013, pp. 3423-3428.
Crossref
[46]
S. Amin, G. A. Schwartz, and H. Tembine, Incentives and security in electricity distribution networks, in Decision and Game Theory for Security, Springer, 2012, pp. 264-280.
Crossref Google Scholar
[47]
A. A. Cárdenas, S. Amin, G. Schwartz, R. Dong, and S. Sastry, A game theory model for electricity theft detection and privacy-aware control in AMI systems, in Proc. IEEE 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2012, pp. 1830-1837.
[48]
M. A. Hearst, S. Dumais, E. Osman, J. Platt, and B. Scholkopf, Support vector machines, IEEE Intelligent Systems and their Applications, vol. 13, no. 4, pp. 18-28, 1998.
Crossref Google Scholar
[49]
V. N. Vapnik, Statistical Learning Theory. Wiley, 1998.
Google Scholar
[50]
S. Depuru, L. Wang, V. Devabhaktuni, and P. Nelapati, A hybrid neural network model and encoding technique for enhanced classification of energy consumption data, in Proc. IEEE Power and Energy Society General Meeting, 2011, pp. 1-8.
[51]
A. Nizar, Z. Dong, J. Zhao, and P. Zhang, A data mining based NTL analysis method, in Proc. IEEE Power Engineering Society General Meeting, 2007, pp. 1-8.
[52]
A. Nizar, Z. Dong, and Y. Wang, Power utility nontechnical loss analysis with extreme learning machine method, IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 946-955, 2008.
Crossref Google Scholar
[53]
A. Nizar and Z. Dong, Identification and detection of electricity customer behaviour irregularities, in Proc. IEEE/PES Power Systems Conference and Exposition, 2009, pp. 1-10.
[54]
R. V. P. Yerra, A. K. Bharathi, P. Rajalakshmi, and U. Desai, WSN based power monitoring in smart grids, in Proc. IEEE Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2011, pp. 401-406.
[55]
P. Kadurek, J. Blom, J. Cobben, and W. Kling, Theft detection and smart metering practices and expectations in the Netherlands, in Proc. 2010 IEEE/PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2010, pp. 1-6.
[56]
R. Jiang, J. Luo, and X. Wang, HRKT: A hierarchical route key tree based group key management for wireless sensor networks, KSII Transaction on Internet and Information Systems, vol. 7, no. 8, pp. 1754-1773, 2013.
Crossref Google Scholar
[57]
C. Shen, H. Zhang, D. Feng, Z. Cao, and J. Huang, Survey of information security, Science in China Series F: Information Sciences, vol. 50, no. 3, pp. 273-298, 2007.
Crossref Google Scholar
[58]
R. Yu, Y. Zhang, S. Gjessing, C. Yuen, S. Xie, and M. Guizani, Cognitive radio based hierarchical communications infrastructure for smart grid, IEEE Network, vol. 25, no. 5, pp. 6-14, 2011.
Crossref Google Scholar
[59]
C. Mitchell, Trusted Computing. Iet, 2005.
Google Scholar
[60]
C. Wang, Q. Wang, K. Ren, and W. Lou, Privacy-preserving public auditing for data storage security in cloud computing, in Proc. IEEE INFOCOM, 2010, pp. 1-9.
[61]
M. Wen, R. Lu, K. Zhang, J. Lei, X. Liang, and X. Shen, PaRQ: A privacy-preserving range query scheme over encrypted metering data for smart grid, IEEE Transaction on Emerging Topics in Computing, vol. 1, no. 1, pp. 178-191, 2013.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 19 Issue 2,
April 2014
Pages 105-120
DOI: 10.1109/TST.2014.6787363
Cite this article:
Jiang R, Lu R, Wang Y, et al. Energy-Theft Detection Issues for Advanced Metering Infrastructure in Smart Grid. Tsinghua Science and Technology, 2014, 19(2): 105-120. https://doi.org/10.1109/TST.2014.6787363

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Received: 25 January 2014
Accepted: 26 January 2014
Published: 15 April 2014
© The author(s) 2014
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