{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T03:15:09Z","timestamp":1775963709418,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Grid Zhejiang Province Technology Project","award":["5211SX220003"],"award-info":[{"award-number":["5211SX220003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In recent years, smart grids have integrated information and communication technologies into power networks, which brings new network security issues. Among the existing cyberattacks, the false data injection attack (FDIA) compromises state estimation in smart grids by injecting false data into the meter measurements, which adversely affects the smart grids. Current studies on FDIAs mainly focus on the detection of its existence, but there are few studies on its localization. Most attack localization methods have difficulty locating the specific bus or line that is under attack quickly and accurately, have high computational complexity and are difficult to apply to large power networks. Therefore, this paper proposes a localization method for FDIAs that is based on a convolutional neural network and optimized with a sparrow search algorithm (SSA\u2013CNN). Based on the physical meaning of measurement vectors, the proposed method can precisely locate a specific bus or line with relatively low computational complexity. To address the difficulty of selecting hyperparameters in the CNN, which leads to the degradation of localization accuracy, a SSA is used to optimize the hyperparameters of the CNN so that the hyperparameters are optimal when using the model for localization. Finally, simulation experiments are conducted on IEEE14-bus and IEEE118-bus test systems, and the simulation results show that the method proposed in this paper has a high localization accuracy and can largely reduce the false-alarm rate.<\/jats:p>","DOI":"10.3390\/info14030180","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T02:33:36Z","timestamp":1678761216000},"page":"180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Localization of False Data Injection Attack in Smart Grids Based on SSA-CNN"],"prefix":"10.3390","volume":"14","author":[{"given":"Kelei","family":"Shen","sequence":"first","affiliation":[{"name":"School of Internet of Things, Jiangnan University, Wuxi 214000, China"}]},{"given":"Wenxu","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Internet of Things, Jiangnan University, Wuxi 214000, China"}]},{"given":"Hongyu","family":"Ni","sequence":"additional","affiliation":[{"name":"School of Internet of Things, Jiangnan University, Wuxi 214000, China"}]},{"given":"Jie","family":"Chu","sequence":"additional","affiliation":[{"name":"School of Internet of Things, Jiangnan University, Wuxi 214000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"ref_1","unstructured":"Qin, B.Y., and Liu, D. (2020, January 18\u201320). Research Progresses and Prospects on Analysis and Control of Cyber-physical System for Power Grid. Proceedings of the CSEE, Lisbon, Portugal."},{"key":"ref_2","first-page":"9","article-title":"A Review of Research on Cyber Attacks and Defense of Power Information Physical Systems(I) Modeling and Assessment","volume":"43","author":"Wang","year":"2019","journal-title":"Autom. Electr. Power Syst."},{"key":"ref_3","unstructured":"Yang, Y.Z., Liu, W.X., Li, C.Z., Liu, G.M., Zang, S., and Zang, Y.W. (2022, January 10\u201312). Review of FDIA Detection Methods for Electric Power SCADA System. Proceedings of the CSEE, Lisbon, Portugal. Available online: https:\/\/kns.cnki.net\/kcms\/detail\/11.2107.tm.20221101.1539.003.html."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/TSG.2011.2161892","article-title":"Integrity Data Attacks in Power Market Operations","volume":"2","author":"Xie","year":"2011","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TSG.2016.2550801","article-title":"Online Data Integrity Attacks Against Real-Time Electrical Market in Smart Grid","volume":"9","author":"Tan","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_6","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":"2016","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_7","first-page":"145","article-title":"Comprehensive Security Assessment for a Cyber Physical Energy System: A Lesson from Ukraine\u2019s Blackout","volume":"40","author":"Guo","year":"2016","journal-title":"Autom. Electr. Power Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/TIFS.2018.2854745","article-title":"Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid","volume":"14","author":"Kurt","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_9","first-page":"2960","article-title":"Detection of False Data Injection Attack in Smart Grid via Adaptive Kalman Filtering","volume":"48","author":"Luo","year":"2022","journal-title":"Acta Autom. Sin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4281","DOI":"10.1109\/TII.2019.2952067","article-title":"Detection and Mitigation of False Data Injection Attacks in Networked Control Systems","volume":"16","author":"Sargolzaei","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3946","DOI":"10.1109\/TIA.2022.3154688","article-title":"A False Data Injection Attack Detection Method for Cooperative Charging Systems","volume":"58","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_12","first-page":"3226","article-title":"Application Research on Pseudo Measurement Modeling and AUKF in FDIAs Identification of Distribution Network","volume":"43","author":"Chen","year":"2019","journal-title":"Power Syst. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1109\/TSG.2022.3141803","article-title":"A Highly Discriminative Detector Against False Data Injection Attacks in AC State Estimation","volume":"13","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"78548","DOI":"10.1109\/ACCESS.2022.3193781","article-title":"False Data Detection in a Clustered Smart Grid Using Unscented Kalman Filter","volume":"10","author":"Rashed","year":"2022","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/JIOT.2020.3005926","article-title":"Interval Observer-Based Detection and Localization Against False Data Injection Attack in Smart Grids","volume":"8","author":"Luo","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3214","DOI":"10.1109\/JIOT.2020.2966221","article-title":"Detection and Isolation of False Data Injection Attacks in Smart Grid via Unknown Input Interval Observer","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/TSG.2022.3148233","article-title":"Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems","volume":"13","author":"Li","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2218","DOI":"10.1109\/TSG.2019.2949998","article-title":"A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids","volume":"11","author":"Musleh","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.ifacol.2018.11.787","article-title":"Physics- and Learning-based Detection and Localization of False Data Injections in Automatic Generation Control","volume":"51","author":"Jevtic","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7040","DOI":"10.1109\/TII.2021.3065080","article-title":"Integrated Cyber and Physical Anomaly Location and Classification in Power Distribution Systems","volume":"17","author":"Ganjkhani","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1109\/TSG.2021.3117977","article-title":"Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids Using Graph Neural Networks","volume":"13","author":"Boyaci","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.35833\/MPCE.2020.000686","article-title":"Defense of Massive False Data Injection Attack via Sparse Attack Points Considering Uncertain Topological Changes","volume":"10","author":"Huang","year":"2022","journal-title":"J. Mod. Power Syst. Clean Energy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1109\/TSG.2017.2647778","article-title":"Applying Hoeffding Adaptive Trees for Real-Time Cyber-Power Event and Intrusion Classification","volume":"9","author":"Adhikari","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3835","DOI":"10.1109\/TPWRD.2021.3138165","article-title":"Augmented State Estimation of Line Parameters in Active Power Distribution Systems with Phasor Measurement Units","volume":"37","author":"Wang","year":"2022","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_25","unstructured":"Liu, X.R., Chang, P., and Sun, Q.Y. (2021, January 21\u201323). Grid False Data Injection Attacks Detection Based on XGBoost and Unscented Kalman Filter Adaptive Hybrid Prediction. Proceedings of the CSEE, Rome, Italy."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8218","DOI":"10.1109\/JIOT.2020.2983911","article-title":"Locational Detection of the False Data Injection Attack in a Smart Grid: A Multilabel Classification Approach","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.54254\/2755-2721\/56\/20240630","article-title":"Comparative study of several new swarm intelligence optimization algorithms","volume":"56","author":"Li","year":"2020","journal-title":"Comput. Eng. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","article-title":"A novel swarm intelligence optimization approach: Sparrow search algorithm","volume":"8","author":"Xue","year":"2020","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1109\/TII.2016.2543145","article-title":"Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid","volume":"12","author":"Jindal","year":"2016","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_30","first-page":"42","article-title":"Detection of power grid disturbances and cyber-attacks based on machine learning","volume":"46","author":"Wang","year":"2019","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4557","DOI":"10.1109\/TPWRS.2019.2919522","article-title":"Integrating Model-Driven and Data-Driven Methods for Power System Frequency Stability Assessment and Control","volume":"34","author":"Wang","year":"2019","journal-title":"IEEE Trans. Power Syst."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/3\/180\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:54:17Z","timestamp":1760122457000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/3\/180"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,14]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["info14030180"],"URL":"https:\/\/doi.org\/10.3390\/info14030180","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,14]]}}}