{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:06:06Z","timestamp":1774677966365,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T00:00:00Z","timestamp":1741910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Special Projects of Henan Province","award":["231111212400"],"award-info":[{"award-number":["231111212400"]}]},{"name":"Key Special Projects of Henan Province","award":["24B520005"],"award-info":[{"award-number":["24B520005"]}]},{"name":"Key Special Projects of Henan Province","award":["2023BS032"],"award-info":[{"award-number":["2023BS032"]}]},{"name":"Natural Science Project of the Henan Provincial Department of Education","award":["231111212400"],"award-info":[{"award-number":["231111212400"]}]},{"name":"Natural Science Project of the Henan Provincial Department of Education","award":["24B520005"],"award-info":[{"award-number":["24B520005"]}]},{"name":"Natural Science Project of the Henan Provincial Department of Education","award":["2023BS032"],"award-info":[{"award-number":["2023BS032"]}]},{"name":"Doctoral Fund Project of Henan University of Technology","award":["231111212400"],"award-info":[{"award-number":["231111212400"]}]},{"name":"Doctoral Fund Project of Henan University of Technology","award":["24B520005"],"award-info":[{"award-number":["24B520005"]}]},{"name":"Doctoral Fund Project of Henan University of Technology","award":["2023BS032"],"award-info":[{"award-number":["2023BS032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>A Cyber-Physical System (CPS) incorporates communication dynamics and software into phsical processes, providing abstractions, modeling, design, and analytical techniques for the system. Based on spatial temporal graph neural networks (STGNNs), anomaly detection technology has been presented to detect anomaly data in smart grids with good performance. However, since topological changes of power networks in smart grids often already predict the occurrence of anomalies, traditional models based on STGNNs to portray network evolution cannot be directly utilized in smart grids. Our research proposed a smart grid anomaly detection method on the grounds of STGNNs, which used evolution in the information of several attributes that affected the power network to represent the evolution of the power network, subsequently used STGNNs to obtain the time-space dependencies of nodes in several information networks, and used a cross-domain method to help the anomaly detection of the power network through anomaly information of other related networks. Laboratory findings reveal that the abnormal data detection rate of our scheme reaches 90% in the initial stage of data transmission and outperforms other comparative methods, and as time goes by, the detection rate becomes higher and higher.<\/jats:p>","DOI":"10.3390\/a18030166","type":"journal-article","created":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T04:29:18Z","timestamp":1741926558000},"page":"166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A GNN-Based False Data Detection Scheme for Smart Grids"],"prefix":"10.3390","volume":"18","author":[{"given":"Junhong","family":"Qiu","sequence":"first","affiliation":[{"name":"XJ Electric Corporation of China Electrical Equipment, Xuchang 461000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5317-2477","authenticated-orcid":false,"given":"Xinxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"The School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"XJ Electric Corporation of China Electrical Equipment, Xuchang 461000, China"}]},{"given":"Huiying","family":"Hou","sequence":"additional","affiliation":[{"name":"The School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China"}]},{"given":"Siyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"XJ Electric Corporation of China Electrical Equipment, Xuchang 461000, China"}]},{"given":"Tiejun","family":"Yang","sequence":"additional","affiliation":[{"name":"The School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lee, E.A. (2008, January 5\u20137). Cyber physical systems: Design challenges. Proceedings of the 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC), Orlando, FL, USA.","DOI":"10.1109\/ISORC.2008.25"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, D., Jin, B., Shi, L., Goh, J., and Ng, S. (2019, January 17\u201319). MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. Proceedings of the 28th International Conference on Artificial Neural Networks, Munich, Germany.","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5615","DOI":"10.1109\/TII.2020.3023430","article-title":"Deepfed: Federated deep learning for intrusion detection in industrial cyber-physical systems","volume":"17","author":"Li","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_4","first-page":"2","article-title":"The 2019 venezuelan blackout and the consequences of cyber uncertainty","volume":"7","author":"Devanny","year":"2020","journal-title":"Rev. Bras. Estud. Def."},{"key":"ref_5","unstructured":"Audibert, J., Michiardi, P., Guyard, F., Marti, S., and Zuluaga, M.A. (2020, January 23\u201327). USAD: Unsupervised anomaly detection on multivariate time series. Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201920), Virtual."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"10364","DOI":"10.1109\/TGRS.2020.3046727","article-title":"Spectral-difference low-rank representation learning for hyperspectral anomaly detection","volume":"59","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1109\/TII.2017.2785963","article-title":"Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids","volume":"14","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_8","first-page":"241","article-title":"Fault diagnosis of power transformers using graph convolutional network","volume":"7","author":"Liao","year":"2021","journal-title":"CSEE J. Power Energy Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Qi, P., Li, D., and Ng, S. (2022, January 9\u201312). MAD-SGCN: Multivariate anomaly detection with self-learning graph convolutional networks. Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICDE53745.2022.00097"},{"key":"ref_10","first-page":"5852","article-title":"Hierarchical attention transfer network for cross-domain sentiment classification","volume":"32","author":"Li","year":"2018","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_11","first-page":"4951","article-title":"Transferable curriculum for weakly-supervised domain adaptation","volume":"33","author":"Shu","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1109\/TNNLS.2021.3110982","article-title":"Cross-domain graph anomaly detection","volume":"33","author":"Ding","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"19210","DOI":"10.1109\/JIOT.2024.3376457","article-title":"Mobile trajectory anomaly detection: Taxonomy, methodology, challenges, and directions","volume":"11","author":"Kong","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4582","DOI":"10.1109\/TII.2023.3326544","article-title":"Causality-guided counterfactual debiasing for anomaly detection of cyber-physical systems","volume":"20","author":"Tang","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1109\/TDSC.2023.3319701","article-title":"Adaptive-correlation-aware unsupervised deep learning for anomaly detection in cyber-physical systems","volume":"21","author":"Xi","year":"2024","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1109\/TCSS.2022.3217790","article-title":"A knowledge-driven anomaly detection framework for social production system","volume":"11","author":"Li","year":"2024","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2728","DOI":"10.1109\/JIOT.2023.3293860","article-title":"An interpretable multivariate time-series anomaly detection method in cyber-physical systems based on adaptive mask","volume":"11","author":"Zhu","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, S., Yu, H., Wang, H., Chai, H., Ma, M., Chen, H., and Zheng, W.X. (2024). Simultaneous diagnosis of Open-Switch and current sensor faults of inverters in IM drives through reduced-order interval iObserver. IEEE Trans. Ind. Electron., 1\u201312.","DOI":"10.1109\/TIE.2024.3485708"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, D., Wang, P., Zhou, J., Sun, L., Du, B., and Fu, Y. (2020, January 17\u201320). Defending water treatment networks: Exploiting spatio-temporal effects for cyber attack detection. Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy.","DOI":"10.1109\/ICDM50108.2020.00012"},{"key":"ref_20","first-page":"608:1","article-title":"MTAD-TF: Multivariate time series anomaly detection using the combination of temporal pattern and feature pattern","volume":"8846","author":"He","year":"2020","journal-title":"Complex"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, H., Wang, Y., Duan, J., Huang, C., Cao, D., Tong, Y., Xu, B., Bai, J., Tong, J., and Zhang, Q. (2020, January 17\u201320). Multivariate time-series anomaly detection via graph attention network. Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy.","DOI":"10.1109\/ICDM50108.2020.00093"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2416","DOI":"10.1109\/TNNLS.2021.3136171","article-title":"Graph convolutional adversarial networks for spatiotemporal anomaly detection","volume":"33","author":"Deng","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1007\/s10618-014-0365-y","article-title":"Graph based anomaly detection and description: A survey","volume":"29","author":"Akoglu","year":"2015","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_24","unstructured":"Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2019, January 6\u20139). How powerful are graph neural networks?. Proceedings of the 7th International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_25","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Seo, Y., Defferrard, M., Vandergheynst, P., and Bresson, X. (2018, January 13\u201316). Structured sequence modeling with graph convolutional recurrent networks. Proceedings of the Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia.","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"ref_27","unstructured":"Chung, J., G\u00fcl\u00e7ehre, \u00c7., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_28","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the NeurIPS 2014, Montreal, QC, Canada."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.1109\/TCYB.2019.2932096","article-title":"Learning graph embedding with adversarial training methods","volume":"50","author":"Pan","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_30","unstructured":"Castillo, C., Mendoza, M., and Poblete, B. (April, January 28). Information credibility on twitter. Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., Wei, Z., Lu, Y., and Wong, K. (2015, January 18\u201323). Detect rumors using time series of social context information on microblogging websites. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne, Australia.","DOI":"10.1145\/2806416.2806607"},{"key":"ref_32","unstructured":"Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B.J., Wong, K., and Cha, M. (2025, February 27). Detecting Rumors from Microblogs with Recurrent Neural Networks. JCAI, Available online: http:\/\/www.ijcai.org\/Abstract\/16\/537."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1609\/aaai.v34i01.5393","article-title":"Rumor detection on social media with bi-directional graph convolutional networks","volume":"34","author":"Bian","year":"2020","journal-title":"AAAI"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102712","DOI":"10.1016\/j.ipm.2021.102712","article-title":"Temporally evolving graph neural network for fake news detection","volume":"58","author":"Song","year":"2021","journal-title":"Inf. Process. Manag."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/3\/166\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:53:23Z","timestamp":1760028803000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/3\/166"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,14]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["a18030166"],"URL":"https:\/\/doi.org\/10.3390\/a18030166","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,14]]}}}