[1] V. G. Vilas, M. Venkat, S. M. Bakre, and V. Velhal, “Smart meter modelling and fault location communication in Smart Grid,” Majlesi Journal of Electrical Engineering, vol. 12, pp. 55–62, 2018.
[2] P. McDaniel, S. McLaughlin, “Security and Privacy Challenges in the Smart Grid”, IEEE Security & Privacy, vol. 7, no. 3, pp. 75-77, 2009.
[3] A. Hasanalizadeh-Khosroshahi, H. Shahinzadeh, “Security Technology by using Firewall for Smart Grid”, Bulletin of Electrical Engineering and Informatics, vol. 5, 2016.
[4] N. R. Babu, “Smart Grid Systems: Modeling and Control”, Apple Academic Press; 1 edition (June 18, 2018).
[5] R. Sowndarya, V. Latha, “An Artificial Intelligent Algorithm for Electricity Theft Detection in AMI”, International Journal of Engineering Science and Computing, vol. 7, no. 3, pp. 5222- 5227, 2017.
[6] G. G. Dranka, P. Ferreira, “Towards a smart grid power system in Brazil: Challenges and opportunities”, Energy Policy, vol. 136, 2020.
[7] E. W. S. Angelos, et al., “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.
[8] E. B. Huerta, et al., “A hybrid GA/SVM approach for gene selection and classification of microarray data”, presented at the Proceedings of the 2006 international conference on Applications of Evolutionary Computing, Budapest, Hungary, 2006.
[9] X. Zhang, et al., “An ACO-based algorithm for parameter optimization of support vector machines”, Expert Syst. Appl.,vol. 37, no. 9, pp. 6618-6628, 2010.
[10] A. Jindal, et al., “Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid”, IEEE Transactions on Industrial Informatics, vol. 12, pp. 1-1, 2016
[11] S.-C. Yip, et al., “Detection of energy theft and defective smart meters in smart grids using linear regression”, International Journal of Electrical Power & Energy Systems, vol. 91, pp. 230-240, 2017.
[12] P. Jokar, et al., “Electricity theft detection in AMI using customers' consumption patterns”, IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 216-226, 2015.
[13] S. Li, et al., “Electricity Theft Detection in Power Grids with Deep Learning and Random Forests”, Journal of Electrical and Computer Engineering, vol. 2019,pp. 1-12,2019.
[14] J. Nagi, et al., “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.
[15] J. Nagi, et al., “Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines”, Power Delivery, IEEE Transactions on, vol. 25, pp. 1162-1171, 2010.
[16] D. R. Pereira, et al., “Social-Spider Optimization-based Support Vector Machines applied for energy theft detection”, Computers & Electrical Engineering, vol. 49, pp. 25-38, 2016.
[17] M. Hasan, et al., “Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach”, Energies, vol. 12, no. 17, p. 3310, 2019.
[18] T. Ahmad, Q. Ul Hasan, “Detection of Frauds and Other Non-technical Losses in Power Utilities using Smart Meters: A Review”, International Journal of Emerging Electric Power Systems, vol. 17, no. 3, pp. 217-234, 2016.
[19] A. A. Aburomman, M. B. Ibne Reaz, “A novel SVM-KNN-PSO ensemble method for intrusion detection system”, Applied Soft Computing, vol. 38, pp. 360-372, 2016.
[20] P. Shi, et al., “A novel intelligent fault diagnosis method of rotating machinery based on deep learning and PSO-SVM”, Journal of Vibro engineering, vol. 19, 2017
[21] J. Du, et al., “A Prediction of Precipitation Data Based on Support Vector Machine and Particle Swarm Optimization (PSO-SVM) Algorithms”, Algorithms, vol. 10, no. 2, 2017.
[22] R. J. Kuo, et al., “Artificial bee colony-based support vector machines with feature selection and parameter optimization for rule extraction”, Knowledge and Information Systems, vol. 55, no. 1, pp. 253-274, 2018.
[23] E. Pourbasheer, et al., “Application of genetic algorithm support vector machine (GA-SVM) for prediction of BK-channels activity”, European Journal of Medicinal Chemistry, vol. 44, no. 12, pp. 5023-5028, 2009.
[24] Y. Prasad, et al., “SVM Classifier Based Feature Selection Using GA, ACO and PSO for siRNA Design”, In: Tan Y., Shi Y., Tan K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg, pp. 307-314,2010.
[25] P. Wang, et al., “PSO-SVM Model Based Prediction and Analysis for the Formation of Navigation Channel Silt”, Applied Mechanics and Materials, vol. 543-547, pp. 4133-4136, 2014.
[26] V. Vapnik, C. Cortes, “Support-Vector Networks”, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[27] A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm”, Computers & Structures, vol. 169, pp. 1-12, 2016.
[28] M. Pardo, and G. Sberveglieri, “Classification of electronic nose data with support vector machines”, Sensors and Actuators B:Chemical, vol. 107, no. 2, pp. 730-737, 2005/06/29/, 2005.
[29] Irish Social Science Data Archive(the smart energy data from the Irish Smart Energy Trial) [Online] Available:http://www.ucd.ie/issda/data/commissionforenergyregulationcer.
[30] UCI datasets://archive.ics.uci.edu/ml/index.php
[31] S.W. Lin, et al., “Particle swarm optimization for parameter determination and feature selection of support vector machines”, Expert System. Appl., vol. 35, no. 4, pp. 1817-1824, 2008.
[32] L. Shuan., et al., “Electricity Theft Detection in Power Grids with Deep Learning and Random Forests”, Journal of Electrical and Computer Engineering, vol. 2019, pp. 1-12, 2019.
[33] M. Nabil., et al., “PPETD: Privacy-Preserving Electricity Theft Detection Scheme With Load Monitoring and Billing for AMI Networks”, IEEE Access, vol. 7, pp. 96334-96348, 2019.
[34] M. Ibrahem., et al., “Efficient Privacy-Preserving Electricity Theft Detection with Dynamic Billing and Load Monitoring for AMI Networks”,arXiv, 2020.