Abstract
Advanced metering infrastructure (AMI) is the primary step to establish a modern smart grid. AMI enables a flexible two-way communication between smart meters and utility company for monitoring and billing purposes. However, AMI suffers from the deceptive behavior of malicious consumers who report false electricity usage in order to reduce their bills, which is known as electricity theft cyber-attacks. In this chapter, we present deep learning-based detectors that can efficiently thwart electricity theft cyber-attacks in smart grid AMI networks. First, we present a customer-specific detector based on a deep feed-forward and recurrent neural networks (RNN). Then, we develop generalized electricity theft detectors that are more robust against contamination attacks compared with customer-specific detectors. In all detectors, optimization of hyperparameters is investigated to improve the performance of the developed detectors. In particular, the hyperparameters of the detectors are optimized via sequential, random, and genetic optimization-based grid search approaches. Extensive test studies are carried out against real energy consumption data to investigate all detectors performance. Also, the performance of the developed deep learning-based detectors is compared with a shallow machine learning approach and a superior performance is observed for the deep learning-based detectors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buevich M, Zhang X, Schnitzer D, Escalada T, Jacquiau-Chamski A, Thacker J, Rowe A (2015) Short paper: microgrid losses: when the whole is greater than the sum of its parts. In: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, pp 95–98. ACM
Viegas JL, Esteves PR, MelÃcio R, Mendes VMF, Vieira SM (2017) Solutions for detection of non-technical losses in the electricity grid: a review. Renew Sust Energ Rev 80:1256–1268
Jokar P, Arianpoo N, Leung VCM (2016) Electricity theft detection in AMI using customers’ consumption patterns. IEEE Trans Smart Grid 7(1):216–226
Singh SK, Bose R, Joshi A (2017) Entropy-based electricity theft detection in AMI network. IET Cyber-Phys Syst Theory Appl 3:99–105
Antmann P (2009) Reducing technical and non-technical losses in the power sector. World Bank, Washington, DC
IBM (2012) Energy theft: incentives to change. Technical report
CBC Electricity theft by B.C. Grow-ops costs 100M a year. Online https://www.cbc.ca/news/canada/british-columbia/electricity-theft-by-b-c-grow-ops-costs-100m-a-year-1.969837. Last accesed Nov 2017
Pickering P E-meters offer multiple ways to combat electricity theft and tampering. Online https://www.electronicdesign.com/meters/e-meters-offer-multiple-ways-combat-electricity-theft-and-tampering. Last accesed Nov 2017
Abaide AR, Canha LN, Barin A, Cassel G (2010) Assessment of the smart grids applied in reducing the cost of distribution system losses. In: 2010 7th International Conference on the European Energy Market (EEM). IEEE, pp 1–6
Nizar AH, Dong ZY, Wang Y (2008) Power utility nontechnical loss analysis with extreme learning machine method. IEEE Trans Power Syst 23(3):946–955
Nagi J, Yap KS, Tiong SK, Ahmed SK, Nagi F (2011) Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system. IEEE Trans Power Delivery 26(2):1284–1285
Ramos C, de Sousa A, Papa J, Falcao X (2011) A new approach for nontechnical losses detection based on optimum-path forest. IEEE Trans Power Syst 26(1):181–189
Angelos E, Saavedra O, Cortés C, de Souza A (2011) Detection and identification of abnormalities in customer consumptions in power distribution systems. IEEE Trans Power Delivery 26(4):2436–2442
Lin C-H, Chen S-J, Kuo C-L, Chen J-L (2014) Non-cooperative game model applied to an advanced metering infrastructure for non-technical loss screening in micro-distribution systems. IEEE Trans Smart Grid 5(5):2468–2469
Amin S, Schwartz GA, Cardenas AA, Sastry SS (2015) Game-theoretic models of electricity theft detection in smart utility networks: providing new capabilities with advanced metering infrastructure. IEEE Control Systems 35(1):66–81
Zhou Y, Chen X, Zomaya AY, Wang L, Hu S (2015) A dynamic programming algorithm for leveraging probabilistic detection of energy theft in smart home. IEEE Trans Emerg Top Comput 3(4):502–513
Liu Y, Hu S (2015) Cyberthreat analysis and detection for energy theft in social networking of smart homes. IEEE Trans Comput Soc Syst 2(4):148–158
Jindal A, Dua A, Kaur K, Singh M, Kumar N, Mishra S (2016) Decision tree and SVM-based data analytics for theft detection in smart grid. IEEE Trans Ind Inf 12(3):1005–1016
Bhat RR, Trevizan RD, Sengupta R, Li X, Bretas A (2016) Identifying nontechnical power loss via spatial and temporal deep learning. In: Proceedings of 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp 272–279
Zhan T-S, Chen S-J, Kao C-C, Kuo C-L, Chen J-L, Lin C-H (2016) Non-technical loss and power blackout detection under advanced metering infrastructure using a cooperative game based inference mechanism. IET Gener Transm Distrib 10(4):873–882
Tariq M, Poor HV (2016) Electricity theft detection and localization in grid-tied microgrids. IEEE Trans Smart Grid
Xiao F, Ai Q (2017) Electricity theft detection in smart grid using random matrix theory. IET Gener Transm Distrib
Dineshkumar K, Ramanathan P, Ramasamy S (2015) Development of ARM processor based electricity theft control system using GSM network. In: 2015 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE, pp 1–6
Ngamchuen S, Pirak C (2013) Smart anti-tampering algorithm design for single phase smart meter applied to AMI systems. In: 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, pp 1–6
Khoo B, Cheng Y (2011) Using RFID for anti-theft in a chinese electrical supply company: a cost-benefit analysis. In: Wireless Telecommunications Symposium (WTS). IEEE, pp 1–6
Henriques HO, Barbero APL, Ribeiro RM, Fortes MZ, Zanco W, Xavier OS, Amorim RM (2014) Development of adapted ammeter for fraud detection in low-voltage installations. Measurement 56:1–7
Devidas AR, Ramesh MV (2010) Wireless smart grid design for monitoring and optimizing electric transmission in india. In: 2010 Fourth International Conference on Sensor Technologies and Applications (SENSORCOMM). IEEE, pp 637–640
Zheng Z, Yang Y, Niu X, Dai H-N, Zhou Y (2018) Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans Ind Inf 14(4):1606–1615
Devices A (2010) Single-phase multifunction metering IC with di/dt sensor interface. MARUTSU, Norwood
Bandim CJ, Alves JER, Pinto AV, Souza FC, Loureiro MRB, Magalhaes CA, Galvez-Durand F (2003) Identification of energy theft and tampered meters using a central observer meter: a mathematical approach. In: Transmission and Distribution Conference and Exposition, 2003 IEEE PES, vol 1. IEEE, pp 163–168
Lo C-H, Ansari N (2013) Consumer: a novel hybrid intrusion detection system for distribution networks in smart grid. IEEE Trans Emer Top Comput 1(1):33–44
Faria LT, Melo JD, Padilha-Feltrin A (2016) Spatial-temporal estimation for nontechnical losses. IEEE Trans Power Delivery 31(1):362–369
Nagi J, Yap KS, Tiong SK, Ahmed SK, Mohamad M (2010) Nontechnical loss detection for metered customers in power utility using support vector machines. IEEE Trans Power Delivery 25(2):1162–1171
Irish Social Science Data Archive. Online https://www.ucd.ie/issda/. Last accesed Nov 2017
He H, Bai Y, Garcia EA, Li S (2008) Adasyn: adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of IEEE International Joint Conference on Computational Intelligence, pp 1322–1328
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge
Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555
Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(Feb):281–305
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
Eschenauer H, Koski J, Osyczka A (2012) Multicriteria design optimization: procedures and applications. Springer Science & Business Media, New York
Pennington J, Schoenholz S, Ganguli S (2017) Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice. In: Advances in Neural Information Processing Systems, pp 4788–4798
Acknowledgements
This publication was made possible by NPRP grant # NPRP9-055-2-022 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Nabil, M., Ismail, M., Mahmoud, M., Shahin, M., Qaraqe, K., Serpedin, E. (2019). Deep Learning-Based Detection of Electricity Theft Cyber-Attacks in Smart Grid AMI Networks. In: Alazab, M., Tang, M. (eds) Deep Learning Applications for Cyber Security. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-13057-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-13057-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13056-5
Online ISBN: 978-3-030-13057-2
eBook Packages: Computer ScienceComputer Science (R0)