{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:02:38Z","timestamp":1777042958921,"version":"3.51.4"},"reference-count":33,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T00:00:00Z","timestamp":1761436800000},"content-version":"vor","delay-in-days":298,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.067818","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T07:43:47Z","timestamp":1756367027000},"page":"4921-4941","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["A Fog-Based Approach for Theft Detection and Zero-Day Attack Prevention in Smart Grid Systems"],"prefix":"10.32604","volume":"85","author":[{"given":"Remah","family":"Younisse","sequence":"first","affiliation":[]},{"given":"Mouhammd","family":"AlKasassbeh","sequence":"additional","affiliation":[]},{"given":"Amjad","family":"Aldweesh","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","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":"ref2","doi-asserted-by":"crossref","first-page":"e515","DOI":"10.1002\/est2.515","article-title":"A novel energy storage and demand side management for entire green smart grid system for NEOM city in Saudi Arabia","volume":"6","author":"Eltamaly","year":"2024","journal-title":"Energy Storage"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"100833","DOI":"10.1016\/j.segan.2022.100833","article-title":"Pattern-based and context-aware electricity theft detection in smart grid","volume":"32","author":"Ahir","year":"2022","journal-title":"Sustain Energy Grids Netw"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/B978-0-323-95054-1.00007-X","author":"Rao","year":"2024","journal-title":"Advanced frequency regulation strategies in renewable-dominated power systems"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"159291","DOI":"10.1109\/ACCESS.2021.3131220","article-title":"Energy theft in smart grids: a survey on data-driven attack strategies and detection methods","volume":"9","author":"Althobaiti","year":"2021","journal-title":"IEEE Access"},{"key":"ref6","series-title":"2025 International Conference on New Trends in Computing Sciences (ICTCS)","first-page":"368","article-title":"Evaluating deep learning for detecting data integrity attacks in energy smart grids","author":"Younisse","year":"2025 Apr 16\u201318"},{"key":"ref7","author":"Khang","year":"2024","journal-title":"Advanced IoT technologies and applications in the industry 4.0 digital economy"},{"key":"ref8","series-title":"2015 IEEE Wireless Communications and Networking Conference (WCNC); 2015 Mar 9\u201312; New Orleans, LA, USA","first-page":"1960","article-title":"SCOOL: a secure traffic congestion control protocol for VANETs","author":"Younes"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"101905","DOI":"10.1016\/j.pmcj.2024.101905","article-title":"Role of IoT technologies in big data management systems: a review and smart grid case study","volume":"100","author":"Al-Ali","year":"2024","journal-title":"Pervasive Mob Comput"},{"key":"ref10","first-page":"581","article-title":"Framework for sustainable energy management using smart grid panels integrated with machine learning and iot based approach","volume":"12","author":"Ahmad","year":"2024","journal-title":"Int J Intell Syst Appl Eng"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"102370","DOI":"10.1016\/j.scs.2020.102370","article-title":"Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid","volume":"62","author":"Babar","year":"2020","journal-title":"Sustain Cities Soc"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"112128","DOI":"10.1016\/j.rser.2022.112128","article-title":"Data-driven probabilistic machine learning in sustainable smart energy\/smart energy systems: key developments, challenges, and future research opportunities in the context of smart grid paradigm","volume":"160","author":"Ahmad","year":"2022","journal-title":"Renew Sustain Energ Rev"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3390\/wevj14020041","article-title":"An object classification approach for autonomous vehicles using machine learning techniques","volume":"14","author":"Alqarqaz","year":"2023","journal-title":"World Electr Veh J"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1080\/09613218.2020.1807896","article-title":"Getting smarter about household energy: the who and what of demand for smart meters","volume":"49","author":"Fettermann","year":"2021","journal-title":"Build Res Inf"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"100341","DOI":"10.1016\/j.cosrev.2020.100341","article-title":"Machine learning and deep learning in smart manufacturing: the smart grid paradigm","volume":"40","author":"Kotsiopoulos","year":"2021","journal-title":"Comput Sci Rev"},{"key":"ref16","first-page":"313","author":"Ahmad","year":"2024","journal-title":"Smart engineering technology and management"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"7846","DOI":"10.3390\/s21237846","article-title":"Cloud-and fog-integrated smart grid model for efficient resource utilisation","volume":"21","author":"Akram","year":"2021","journal-title":"Sensors"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/TIFS.2020.3014487","article-title":"Smart and practical privacy-preserving data aggregation for fog-based smart grids","volume":"16","author":"Zhao","year":"2020","journal-title":"IEEE Trans Inf Forens Secur"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jksuci.2022.05.007","article-title":"Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment","volume":"35","author":"Zidi","year":"2023","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.comcom.2022.11.001","article-title":"A review of machine learning-based zero-day attack detection: challenges and future directions","volume":"198","author":"Guo","year":"2023","journal-title":"Comput Commun"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.3390\/electronics9101684","article-title":"Utilising deep learning techniques for effective zero-day attack detection","volume":"9","author":"Hindy","year":"2020","journal-title":"Electronics"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"3114","DOI":"10.1109\/ACCESS.2023.3349091","article-title":"A secure and resilient smart energy meter","volume":"12","author":"Hseiki","year":"2024","journal-title":"IEEE Access"},{"key":"ref23","first-page":"103775","article-title":"Hybrid non-technical-loss detection in fog-enabled smart grids","volume":"65","author":"Khan","year":"2024","journal-title":"Sustain Energy Technol Assess"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"3832","DOI":"10.3390\/en13153832","article-title":"Energy theft detection in advanced metering infrastructure based on anomaly pattern detection","volume":"13","author":"Park","year":"2020","journal-title":"Energies"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.egyr.2022.11.072","article-title":"Electricity-theft detection for smart grid security using smart meter data: a deep-CNN based approach","volume":"9","author":"Haq","year":"2023","journal-title":"Energy Rep"},{"key":"ref26","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":"ref27","doi-asserted-by":"crossref","first-page":"109088","DOI":"10.1016\/j.ijepes.2023.109088","article-title":"A two stage approach to electricity theft detection in AMI using deep learning","volume":"150","author":"Emadaleslami","year":"2023","journal-title":"Int J Electr Power Energy Syst"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"7659","DOI":"10.1109\/JIOT.2019.2903312","article-title":"Energy theft detection with energy privacy preservation in the smart grid","volume":"6","author":"Yao","year":"2019","journal-title":"IEEE Internet Things J"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"98427","DOI":"10.1109\/ACCESS.2022.3206367","article-title":"Anomaly detection based on CNN and regularization techniques against zero-day attacks in IoT networks","volume":"10","author":"Hairab","year":"2022","journal-title":"IEEE Access"},{"key":"ref30","series-title":"2012 IEEE Global Communications Conference (GLOBECOM)","first-page":"73","article-title":"A distributed infrastructure-based congestion avoidance protocol for Vehicular Ad Hoc Networks","author":"Younes","year":"2012 Dec 3\u20137"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"6069","DOI":"10.1109\/JIOT.2021.3110784","article-title":"Feddetect: a novel privacy-preserving federated learning framework for energy theft detection in smart grid","volume":"9","author":"Wen","year":"2021","journal-title":"IEEE Internet Things J"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"3236","DOI":"10.3390\/s24103236","article-title":"Deep anomaly detection framework utilizing federated learning for electricity theft zero-day cyberattacks","volume":"24","author":"Alshehri","year":"2024","journal-title":"Sensors"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"5799","DOI":"10.1007\/s10586-024-04272-2","article-title":"Spark-based multi-verse optimizer as wrapper features selection algorithm for phishing attack challenge","volume":"27","author":"Al-Sawwa","year":"2024","journal-title":"Clust Comput"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-85-3\/TSP_CMC_67818\/TSP_CMC_67818.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:20:53Z","timestamp":1763346053000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v85n3\/64161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.067818","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2025-05-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-25","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-23","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}