{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:39:38Z","timestamp":1771025978827,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030797249","type":"print"},{"value":"9783030797256","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-79725-6_38","type":"book-chapter","created":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T03:46:28Z","timestamp":1624938388000},"page":"383-394","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Detection of Non-Technical Losses Using MLP-GRU Based Neural Network to Secure Smart Grids"],"prefix":"10.1007","author":[{"given":"Benish","family":"Kabir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Pamir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashraf","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shoaib","family":"Munawar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Asif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadeem","family":"Javaid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"issue":"3","key":"38_CR1","doi-asserted-by":"publisher","first-page":"2661","DOI":"10.1109\/TSG.2018.2807925","volume":"10","author":"MM Buzau","year":"2018","unstructured":"Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P., G\u00f3mez-Exp\u00f3sito, A.: Detection of non-technical losses using smart meter data and supervised learning. IEEE Trans. Smart Grid 10(3), 2661\u20132670 (2018)","journal-title":"IEEE Trans. Smart Grid"},{"key":"38_CR2","doi-asserted-by":"publisher","unstructured":"Kong, X., Zhao, X., Liu, C., Li, Q., Dong, D., Li, Y.: Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM. Int. J. Electr. Power Energy Syst. 125 (2021). https:\/\/doi.org\/10.1016\/j.ijepes.2020.106544","DOI":"10.1016\/j.ijepes.2020.106544"},{"issue":"4","key":"38_CR3","doi-asserted-by":"publisher","first-page":"1606","DOI":"10.1109\/TII.2017.2785963","volume":"14","author":"Z Zheng","year":"2017","unstructured":"Zheng, Z., Yang, Y., Niu, X., Dai, H.N., Zhou, Y.: Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans. Industr. Inf. 14(4), 1606\u20131615 (2017)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"2","key":"38_CR4","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/TPWRS.2019.2943115","volume":"35","author":"MM Buzau","year":"2019","unstructured":"Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P., G\u00f3mez-Exp\u00f3sito, A.: Hybrid deep neural networks for detection of non-technical losses in electricity smart meters. IEEE Trans. Power Syst. 35(2), 1254\u20131263 (2019)","journal-title":"IEEE Trans. Power Syst."},{"issue":"2","key":"38_CR5","doi-asserted-by":"publisher","first-page":"2326","DOI":"10.1109\/TSG.2019.2892595","volume":"10","author":"R Punmiya","year":"2019","unstructured":"Punmiya, R., Choe, S.: Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing. IEEE Trans. Smart Grid 10(2), 2326\u20132329 (2019)","journal-title":"IEEE Trans. Smart Grid"},{"key":"38_CR6","first-page":"1","volume":"70","author":"Z Yan","year":"2021","unstructured":"Yan, Z., Wen, H.: Electricity theft detection base on extreme gradient boosting in AMI. IEEE Trans. Instrum. Meas. 70, 1\u20139 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"6","key":"38_CR7","doi-asserted-by":"publisher","first-page":"7171","DOI":"10.1109\/TPWRS.2018.2853162","volume":"33","author":"NF Avila","year":"2018","unstructured":"Avila, N.F., Figueroa, G., Chu, C.C.: NTL detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random undersampling boosting. IEEE Trans. Power Syst. 33(6), 7171\u20137180 (2018)","journal-title":"IEEE Trans. Power Syst."},{"issue":"1","key":"38_CR8","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1109\/TSG.2015.2425222","volume":"7","author":"P Jokar","year":"2015","unstructured":"Jokar, P., Arianpoo, N., Leung, V.C.: Electricity theft detection in AMI using customers\u2019 consumption patterns. IEEE Trans. Smart Grid 7(1), 216\u2013226 (2015)","journal-title":"IEEE Trans. Smart Grid"},{"key":"38_CR9","doi-asserted-by":"publisher","unstructured":"Li, S., Han, Y., Yao, X., Yingchen, S., Wang, J., Zhao, Q.: Electricity theft detection in power grids with deep learning and random forests. J. Electr. Comput. Eng. 2019 (2019). https:\/\/doi.org\/10.1155\/2019\/4136874","DOI":"10.1155\/2019\/4136874"},{"issue":"17","key":"38_CR10","doi-asserted-by":"publisher","first-page":"3310","DOI":"10.3390\/en12173310","volume":"12","author":"M Hasan","year":"2019","unstructured":"Hasan, M., Toma, R.N., Nahid, A.A., Islam, M.M., Kim, J.M.: Electricity theft detection in smart grid systems: a CNN-LSTM based approach. Energies 12(17), 3310 (2019). https:\/\/doi.org\/10.3390\/en12173310","journal-title":"Energies"},{"key":"38_CR11","doi-asserted-by":"publisher","first-page":"9645","DOI":"10.1109\/ACCESS.2019.2891315","volume":"7","author":"G Fenza","year":"2019","unstructured":"Fenza, G., Gallo, M., Loia, V.: Drift-aware methodology for anomaly detection in smart grid. IEEE Access 7, 9645\u20139657 (2019)","journal-title":"IEEE Access"},{"issue":"3","key":"38_CR12","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1109\/TII.2018.2873814","volume":"15","author":"K Zheng","year":"2018","unstructured":"Zheng, K., Chen, Q., Wang, Y., Kang, C., Xia, Q.: A novel combined data-driven approach for electricity theft detection. IEEE Trans. Industr. Inf. 15(3), 1809\u20131819 (2018)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"8","key":"38_CR13","doi-asserted-by":"publisher","first-page":"860","DOI":"10.3390\/electronics8080860","volume":"8","author":"MS Saeed","year":"2019","unstructured":"Saeed, M.S., Mustafa, M.W., Sheikh, U.U., Jumani, T.A., Mirjat, N.H.: Ensemble bagged tree based classification for reducing non-technical losses in multan electric power company of Pakistan. Electronics 8(8), 860 (2019). https:\/\/doi.org\/10.3390\/electronics8080860","journal-title":"Electronics"},{"issue":"3","key":"38_CR14","doi-asserted-by":"publisher","first-page":"5531","DOI":"10.1109\/JIOT.2019.2903281","volume":"6","author":"W Li","year":"2019","unstructured":"Li, W., Logenthiran, T., Phan, V.T., Woo, W.L.: A novel smart energy theft system (SETS) for IoT-based smart home. IEEE Internet Things J. 6(3), 5531\u20135539 (2019)","journal-title":"IEEE Internet Things J."},{"issue":"21","key":"38_CR15","doi-asserted-by":"publisher","first-page":"5758","DOI":"10.3390\/en13215758","volume":"13","author":"X Feng","year":"2020","unstructured":"Feng, X., et al.: A novel electricity theft detection scheme based on text convolutional neural networks. Energies 13(21), 5758 (2020). https:\/\/doi.org\/10.3390\/en13215758","journal-title":"Energies"},{"issue":"8","key":"38_CR16","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.3390\/en13082039","volume":"13","author":"Z Qu","year":"2020","unstructured":"Qu, Z., Li, H., Wang, Y., Zhang, J., Abu-Siada, A., Yao, Y.: Detection of electricity theft behavior based on improved synthetic minority oversampling technique and random forest classifier. Energies 13(8), 2039 (2020). https:\/\/doi.org\/10.3390\/en13082039","journal-title":"Energies"},{"key":"38_CR17","doi-asserted-by":"publisher","unstructured":"Gunturi, S.K., Sarkar, D.: Ensemble machine learning models for the detection of energy theft. Electric Power Syst. Res. 106904 (2020). https:\/\/doi.org\/10.1016\/j.epsr.2020.106904","DOI":"10.1016\/j.epsr.2020.106904"},{"key":"38_CR18","doi-asserted-by":"publisher","unstructured":"Huang, Y., Xu, Q.: Electricity theft detection based on stacked sparse denoising autoencoder. Int. J. Electr. Power Energy Syst. 125 (2021). https:\/\/doi.org\/10.1016\/j.ijepes.2020.106448","DOI":"10.1016\/j.ijepes.2020.106448"},{"issue":"17","key":"38_CR19","doi-asserted-by":"publisher","first-page":"4291","DOI":"10.3390\/en13174291","volume":"13","author":"X Gong","year":"2020","unstructured":"Gong, X., Tang, B., Zhu, R., Liao, W., Song, L.: Data augmentation for electricity theft detection using conditional variational auto-encoder. Energies 13(17), 4291 (2020). https:\/\/doi.org\/10.3390\/en13174291","journal-title":"Energies"},{"issue":"15","key":"38_CR20","doi-asserted-by":"publisher","first-page":"3832","DOI":"10.3390\/en13153832","volume":"13","author":"CH Park","year":"2020","unstructured":"Park, C.H., Kim, T.: Energy theft detection in advanced metering infrastructure based on anomaly pattern detection. Energies 13(15), 3832 (2020). https:\/\/doi.org\/10.3390\/en13153832","journal-title":"Energies"},{"issue":"12","key":"38_CR21","doi-asserted-by":"publisher","first-page":"4378","DOI":"10.3390\/app10124378","volume":"10","author":"M Adil","year":"2020","unstructured":"Adil, M., Javaid, N., Qasim, U., Ullah, I., Shafiq, M., Choi, J.G.: LSTM and bat-based RUSBoost approach for electricity theft detection. Appl. Sci. 10(12), 4378 (2020). https:\/\/doi.org\/10.3390\/app10124378","journal-title":"Appl. Sci."},{"issue":"1","key":"38_CR22","doi-asserted-by":"publisher","first-page":"15","DOI":"10.32604\/cmc.2019.06497","volume":"60","author":"A Maamar","year":"2019","unstructured":"Maamar, A., Benahmed, K.: A hybrid model for anomalies detection in AMI system combining K-means clustering and deep neural network. Comput. Mater. Continua 60(1), 15\u201339 (2019)","journal-title":"Comput. Mater. Continua"},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Ding, N., Ma, H., Gao, H., Ma, Y., Tan, G.: Real-time anomaly detection based on long short-Term memory and Gaussian Mixture Model. Comput. Electr. Eng. 79 (2019)","DOI":"10.1016\/j.compeleceng.2019.106458"},{"issue":"3","key":"38_CR24","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1109\/TII.2016.2543145","volume":"12","author":"A Jindal","year":"2016","unstructured":"Jindal, A., Dua, A., Kaur, K., Singh, M., Kumar, N., Mishra, S.: Decision tree and SVM-based data analytics for theft detection in smart grid. IEEE Trans. Industr. Inf. 12(3), 1005\u20131016 (2016)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"18","key":"38_CR25","doi-asserted-by":"publisher","first-page":"3452","DOI":"10.3390\/en12183452","volume":"12","author":"X Lu","year":"2019","unstructured":"Lu, X., Zhou, Y., Wang, Z., Yi, Y., Feng, L., Wang, F.: Knowledge embedded semi-supervised deep learning for detecting non-technical losses in the smart grid. Energies 12(18), 3452 (2019). https:\/\/doi.org\/10.3390\/en12183452","journal-title":"Energies"},{"key":"38_CR26","unstructured":"Arif, A., Javaid, N., Aldegheishem, A., Alrajeh, N.: Big Data Analytics for Identifying Electricity Theft using Machine Learning Approaches in Micro Grids for Smart Communities"},{"key":"38_CR27","doi-asserted-by":"crossref","unstructured":"Ghori, K.M., Imran, M., Nawaz, A., Abbasi, R.A., Ullah, A., Szathmary, L.: Performance analysis of machine learning classifiers for non-technical loss detection. J. Ambient Intell. Hum. Comput. 1\u201316 (2020)","DOI":"10.1007\/s12652-019-01649-9"},{"key":"38_CR28","doi-asserted-by":"publisher","first-page":"221767","DOI":"10.1109\/ACCESS.2020.3042636","volume":"8","author":"Z Aslam","year":"2020","unstructured":"Aslam, Z., Ahmed, F., Almogren, A., Shafiq, M., Zuair, M., Javaid, N.: An attention guided semi-supervised learning mechanism to detect electricity frauds in the distribution systems. IEEE Access 8, 221767\u2013221782 (2020)","journal-title":"IEEE Access"},{"key":"38_CR29","doi-asserted-by":"publisher","first-page":"25036","DOI":"10.1109\/ACCESS.2021.3056566","volume":"9","author":"A Aldegheishem","year":"2021","unstructured":"Aldegheishem, A., Anwar, M., Javaid, N., Alrajeh, N., Shafiq, M., Ahmed, H.: Towards sustainable energy efficiency with intelligent electricity theft detection in smart grids emphasising enhanced neural networks. IEEE Access 9, 25036\u201325061 (2021)","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Networks and Systems","Complex, Intelligent and Software Intensive Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-79725-6_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T03:58:14Z","timestamp":1624939094000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-79725-6_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030797249","9783030797256"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-79725-6_38","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CISIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference on Complex, Intelligent, and Software Intensive Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"coisis2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/voyager.ce.fit.ac.jp\/conf\/cisis\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}