{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T11:50:23Z","timestamp":1774266623985,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T00:00:00Z","timestamp":1665705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples\u2019 nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model\u2019s efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors.<\/jats:p>","DOI":"10.3390\/s22207818","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"7818","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU\u2013BiLSTM Model with Feature Engineering-Based Preprocessing"],"prefix":"10.3390","volume":"22","author":[{"given":"Shoaib","family":"Munawar","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, International Islamic University, Islamabad 44000, Pakistan"}]},{"given":"Nadeem","family":"Javaid","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan"}]},{"given":"Zeshan Aslam","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, International Islamic University, Islamabad 44000, Pakistan"}]},{"given":"Naveed Ishtiaq","family":"Chaudhary","sequence":"additional","affiliation":[{"name":"Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9953-822X","authenticated-orcid":false,"given":"Muhammad Asif Zahoor","family":"Raja","sequence":"additional","affiliation":[{"name":"Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1926-9486","authenticated-orcid":false,"given":"Ahmad H.","family":"Milyani","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"given":"Abdullah","family":"Ahmed Azhari","sequence":"additional","affiliation":[{"name":"The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Grigsby, L.L. (2007). Electric Power Generation, Transmission, and Distribution, CRC Press.","DOI":"10.1201\/9781420009255"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/MIE.2011.942176","article-title":"The new frontier of smart grids","volume":"5","author":"Yu","year":"2011","journal-title":"IEEE Ind. Electron. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1016\/j.enpol.2010.11.037","article-title":"Electricity theft: Overview, issues, prevention and a smart meter based approach to control theft","volume":"39","author":"Depuru","year":"2011","journal-title":"Energy Policy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/TPWRS.2019.2943115","article-title":"Hybrid deep neural networks for detection of Non-Technical Losses in electricity Smart Meters","volume":"35","author":"Buzau","year":"2019","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_5","unstructured":"World Bank (2003). World Development Report 2004: Making Services Work for Poor People, The World Bank."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.enpol.2016.02.048","article-title":"The determinants of electricity theft: An empirical analysis of Indian states","volume":"93","author":"Gaur","year":"2016","journal-title":"Energy Policy"},{"key":"ref_7","unstructured":"Ag\u00fcero, J.R. (2012, January 7\u201310). Improving the efficiency of power distribution systems through technical and Non-Technical Losses reduction. Proceedings of the PES T&D 2012, Orlando, FL, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1016\/j.rser.2017.05.193","article-title":"Solutions for detection of Non-Technical Losses in the electricity grid: A review","volume":"80","author":"Viegas","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Munawar, S., Asif, M., Kabir, B., Ullah, A., and Javaid, N. (2021). Electricity Theft Detection in Smart Meters Using a Hybrid Bi-directional GRU Bi-directional LSTM Model. Conference on Complex, Intelligent, and Software Intensive Systems, Springer.","DOI":"10.1007\/978-3-030-79725-6_29"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Salinas, S., Li, M., and Li, P. (2012, January 18\u201321). Privacy-preserving energy theft detection in smart grids. Proceedings of the 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Seoul, Korea.","DOI":"10.1109\/SECON.2012.6275834"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Saeed, M.S., Mustafa, M.W., Sheikh, U.U., Jumani, T.A., and Mirjat, N.H. (2019). Ensemble bagged tree based classification for reducing Non-Technical Losses in multan electric power company of Pakistan. Electronics, 8.","DOI":"10.3390\/electronics8080860"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.1109\/TSG.2019.2892595","article-title":"Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing","volume":"10","author":"Punmiya","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7171","DOI":"10.1109\/TPWRS.2018.2853162","article-title":"NTL detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random undersampling boosting","volume":"33","author":"Avila","year":"2018","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Adil, M., Javaid, N., Qasim, U., Ullah, I., Shafiq, M., and Choi, J.G. (2020). LSTM and bat-based RUSBoost approach for Electricity Theft Detection. Appl. Sci., 10.","DOI":"10.3390\/app10124378"},{"key":"ref_15","first-page":"4136874","article-title":"Electricity Theft Detection in power grids with deep learning and Random Forests","volume":"2019","author":"Li","year":"2019","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2453","DOI":"10.1109\/TIFS.2020.2965276","article-title":"Hidden electricity theft by exploiting multiple-pricing scheme in smart grids","volume":"15","author":"Liu","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106544","DOI":"10.1016\/j.ijepes.2020.106544","article-title":"Electricity Theft Detection in low-voltage stations based on similarity measure and DT-KSVM","volume":"125","author":"Kong","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_18","first-page":"1","article-title":"Electricity Theft Detection base on extreme gradient boosting in AMI","volume":"70","author":"Yan","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106904","DOI":"10.1016\/j.epsr.2020.106904","article-title":"Ensemble Machine Learning models for the detection of energy theft","volume":"192","author":"Gunturi","year":"2021","journal-title":"Electr. Power Syst. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"39638","DOI":"10.1109\/ACCESS.2022.3166146","article-title":"Electricity Theft Detection in Smart Grids Based on Deep Neural Network","volume":"10","author":"Lepolesa","year":"2022","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yao, R., Wang, N., Ke, W., Chen, P., and Sheng, X. (2022). Electricity Theft Detection in unbalanced sample distribution: A novel approach including a mechanism of sample augmentation. Appl. Intell., 1\u201320.","DOI":"10.1007\/s10489-022-04069-z"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liao, W., Yang, Z., Liu, K., Zhang, B., Chen, X., and Song, R. (2022). Electricity Theft Detection Using Euclidean and Graph Convolutional Neural Networks. IEEE Trans. Power Syst., 1\u201313.","DOI":"10.1109\/TPWRS.2022.3196403"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4568","DOI":"10.1109\/TPWRS.2022.3150050","article-title":"Electricity Theft Detection in AMI with Low False Positive Rate Based on Deep Learning and Evolutionary Algorithm","volume":"37","author":"Gu","year":"2022","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1109\/TII.2018.2873814","article-title":"A novel combined data-driven approach for Electricity Theft Detection","volume":"15","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Aslam, Z., Javaid, N., Ahmad, A., Ahmed, A., and Gulfam, S.M. (2020). A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids. Energies, 13.","DOI":"10.3390\/en13215599"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106448","DOI":"10.1016\/j.ijepes.2020.106448","article-title":"Electricity Theft Detection based on stacked sparse denoising autoencoder","volume":"125","author":"Huang","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.1109\/ACCESS.2019.2891315","article-title":"Drift-aware methodology for anomaly detection in smart grid","volume":"7","author":"Fenza","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.ijepes.2017.04.005","article-title":"Detection of energy theft and defective Smart Meters in smart grids using linear regression","volume":"91","author":"Yip","year":"2017","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Park, C.H., and Kim, T. (2020). Energy Theft Detection in Advanced Metering Infrastructure Based on Anomaly Pattern Detection. Energies, 13.","DOI":"10.3390\/en13153832"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hu, J., Li, S., Hu, J., and Yang, G. (2018). A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification. Sustainability, 10.","DOI":"10.3390\/su10010219"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hasan, M., Toma, R.N., Nahid, A.A., Islam, M.M., and Kim, J.M. (2019). Electricity Theft Detection in smart grid systems: A CNN-LSTM based approach. Energies, 12.","DOI":"10.3390\/en12173310"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Khalid, R., Javaid, N., Al-Zahrani, F.A., Aurangzeb, K., Qazi, E.U.H., and Ashfaq, T. (2020). Electricity load and price forecasting using Jaya-Long Short Term Memory (JLSTM) in smart grids. Entropy, 22.","DOI":"10.3390\/e22010010"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3687","DOI":"10.1109\/TSG.2018.2834150","article-title":"Probabilistic energy management for building climate comfort in smart thermal grids with seasonal storage systems","volume":"10","author":"Rostampour","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1109\/TSG.2015.2425222","article-title":"Electricity Theft Detection in AMI using customers\u2019 consumption patterns","volume":"7","author":"Jokar","year":"2015","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.1109\/TSG.2018.2807925","article-title":"Detection of Non-Technical Losses using smart meter data and supervised learning","volume":"10","author":"Buzau","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1109\/TSG.2019.2961136","article-title":"Electricity theft pinpointing through correlation analysis of master and individual meter readings","volume":"11","author":"Biswas","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3428","DOI":"10.1109\/TSG.2020.2973681","article-title":"Deep learning detection of electricity theft cyber-attacks in renewable Distributed Generation","volume":"11","author":"Ismail","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"105739","DOI":"10.1016\/j.asoc.2019.105739","article-title":"A hybrid VMD\u2013BiGRU model for rubber futures time series forecasting","volume":"84","author":"Zhu","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bhagat, R.C., and Patil, S.S. (2015, January 12\u201313). Enhanced SMOTE algorithm for classification of imbalanced big-data using Random Forest. Proceedings of the 2015 IEEE International Advance Computing Conference (IACC), Bangalore, India.","DOI":"10.1109\/IADCC.2015.7154739"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"10348","DOI":"10.1109\/TVT.2019.2925562","article-title":"Behavioral modeling and linearization of wideband RF power amplifiers using BiLSTM networks for 5G wireless systems","volume":"68","author":"Sun","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4425","DOI":"10.1016\/j.egyr.2021.07.008","article-title":"A novel feature engineered-CatBoost-based supervised Machine Learning framework for Electricity Theft Detection","volume":"7","author":"Hussain","year":"2021","journal-title":"Energy Rep."},{"key":"ref_42","unstructured":"Ullah, A., Munawar, S., Asif, M., Kabir, B., and Javaid, N. (2021). Synthetic theft attacks implementation for data balancing and a gated recurrent unit based Electricity Theft Detection in smart grids. Conference on Complex, Intelligent, and Software Intensive Systems, Springer."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Asif, M., Kabir, B., Ullah, A., Munawar, S., and Javaid, N. (2021). Towards Energy Efficient Smart Grids: Data Augmentation Through BiWGAN, Feature Extraction and Classification Using Hybrid 2DCNN and BiLSTM. International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Springer.","DOI":"10.1007\/978-3-030-79728-7_12"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Asif, M., Ullah, A., Munawar, S., Kabir, B., Khan, A., and Javaid, N. (2021). Alexnet-AdaBoost-ABC based hybrid neural network for Electricity Theft Detection in smart grids. Conference on Complex, Intelligent, and Software Intensive Systems, Springer.","DOI":"10.1007\/978-3-030-79725-6_24"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kabir, B., Ullah, A., Munawar, S., Asif, M., and Javaid, N. (2021). Detection of Non-Technical Losses Using MLP-GRU Based Neural Network to Secure Smart Grids. Conference on Complex, Intelligent, and Software Intensive Systems, Springer.","DOI":"10.1007\/978-3-030-79725-6_38"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Dash, S.K., Roccotelli, M., Khansama, R.R., Fanti, M.P., and Mangini, A.M. (2021). Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method. Appl. Sci., 11.","DOI":"10.3390\/app11188612"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Khan, Z.A., Adil, M., Javaid, N., Saqib, M.N., Shafiq, M., and Choi, J.G. (2020). Electricity Theft Detection using supervised learning techniques on smart meter data. Sustainability, 12.","DOI":"10.3390\/su12198023"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2778","DOI":"10.3390\/en15082778","article-title":"Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit","volume":"15","author":"Javaid","year":"2022","journal-title":"Energies"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gul, H., Javaid, N., Ullah, I., Qamar, A.M., Afzal, M.K., and Joshi, G.P. (2020). Detection of Non-Technical Losses using SOSTLink and bidirectional gated recurrent unit to secure Smart Meters. Appl. Sci., 10.","DOI":"10.3390\/app10093151"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/7818\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:54:27Z","timestamp":1760144067000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/7818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,14]]},"references-count":49,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22207818"],"URL":"https:\/\/doi.org\/10.3390\/s22207818","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,14]]}}}