{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T10:05:50Z","timestamp":1779617150396,"version":"3.53.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698837","type":"print"},{"value":"9789819698844","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-9884-4_31","type":"book-chapter","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T12:24:40Z","timestamp":1753446280000},"page":"370-381","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Anomaly Detection Model for Edge Network Infrastructure Based on Time Series"],"prefix":"10.1007","author":[{"given":"Yuxiang","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijie","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"issue":"10","key":"31_CR1","doi-asserted-by":"publisher","first-page":"14244","DOI":"10.1109\/JIOT.2025.3525815","volume":"12","author":"Z Zhan","year":"2025","unstructured":"Zhan, Z., Ma, D., Hu, X., Zhang, S.: An online collaborative imputation method for industrial missing data based on multiscale MatGAN in edge computing. IEEE Internet Things J. 12(10), 14244\u201314253 (2025)","journal-title":"IEEE Internet Things J."},{"key":"31_CR2","unstructured":"Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In:\u00a0International Conference on Learning Representations (ICLR)\u00a0(Poster) (2018)"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Revisiting VAE for unsupervised time series anomaly detection: a frequency perspective. In: Proceedings of the ACM Web Conference 2024, pp. 3096\u20133105 (2024)","DOI":"10.1145\/3589334.3645710"},{"key":"31_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111507","volume":"290","author":"H Kang","year":"2024","unstructured":"Kang, H., Kang, P.: Transformer-based multivariate time series anomaly detection using inter-variable attention mechanism. Knowl.-Based Syst. 290, 111507 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Yang, Y., et al.: DCDetector: dual attention contrastive representation learning for time series anomaly detection. In:\u00a0Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3033\u20133045 (2023)","DOI":"10.1145\/3580305.3599295"},{"key":"31_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110874","volume":"157","author":"ZZ Darban","year":"2025","unstructured":"Darban, Z.Z., Webb, G.I., Pan, S., Aggarwal, C.C., Salehi, M.: CARLA: self-supervised contrastive representation learning for time series anomaly detection. Pattern Recogn. 157, 110874 (2025)","journal-title":"Pattern Recogn."},{"key":"31_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102255","volume":"106","author":"Y Zheng","year":"2024","unstructured":"Zheng, Y., et al.: Graph spatiotemporal process for multivariate time series anomaly detection with missing values. Inf. Fusion 106, 102255 (2024)","journal-title":"Inf. Fusion"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Xiao, C., Gou, Z., Tai, W., Zhang, K., Zhou, F.: Imputation-based time-series anomaly detection with conditional weight-incremental diffusion models. In:\u00a0Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2742\u20132751 (2023)","DOI":"10.1145\/3580305.3599391"},{"key":"31_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111215","volume":"283","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Xu, X., Hu, L., Fan, J., Han, M.: A time series continuous missing values imputation method based on generative adversarial networks. Knowl.-Based Syst. 283, 111215 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"31_CR10","first-page":"17804","volume":"33","author":"L Bai","year":"2020","unstructured":"Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. Adv. Neural. Inf. Process. Syst. 33, 17804\u201317815 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"31_CR11","unstructured":"Van Den Oord, A., Dieleman, S., Zen, H., et al.: WaveNet: a generative model for raw audio. arXiv preprint arXiv:1609.03499. 12 (2016)"},{"key":"31_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.130040","volume":"637","author":"X Zhang","year":"2025","unstructured":"Zhang, X., Pan, L., Shen, Q., Liu, Z., Lou, J., Jiang, Y.: Trend-aware spatio-temporal fusion graph convolutional network with self-attention for traffic prediction. Neurocomputing 637, 130040 (2025)","journal-title":"Neurocomputing"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., Pei, D.: Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In:\u00a0Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2828\u20132837 (2019)","DOI":"10.1145\/3292500.3330672"},{"issue":"6","key":"31_CR14","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s10489-025-06412-6","volume":"55","author":"C Gao","year":"2025","unstructured":"Gao, C., Ma, H., Pei, Q., Chen, Y.: Dynamic graph-based graph attention network for anomaly detection in industrial multivariate time series data. Appl. Intell. 55(6), 517 (2025)","journal-title":"Appl. Intell."},{"issue":"13","key":"31_CR15","doi-asserted-by":"publisher","first-page":"3738","DOI":"10.3390\/s20133738","volume":"20","author":"Z Niu","year":"2020","unstructured":"Niu, Z., Yu, K., Wu, X.: LSTM-based VAE-GAN for time-series anomaly detection. Sensors 20(13), 3738 (2020)","journal-title":"Sensors"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Zhao, H., et al.: Multivariate time-series anomaly detection via graph attention network. In:\u00a0IEEE International Conference on Data Mining (ICDM), pp. 841\u2013850 (2020)","DOI":"10.1109\/ICDM50108.2020.00093"},{"key":"31_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120852","volume":"676","author":"S He","year":"2024","unstructured":"He, S., Du, M., Jiang, X., Zhang, W., Wang, C.: VAEAT: variational autoencoder with adversarial training for multivariate time series anomaly detection. Inf. Sci. 676, 120852 (2024)","journal-title":"Inf. Sci."},{"issue":"13","key":"31_CR18","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521\u20133526 (2017)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"31_CR19","unstructured":"Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. In:\u00a0Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS), pp. 6470\u20136479 (2017)"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, X., Song, D., Tao, D.: Sparsified subgraph memory for continual graph representation learning. In:\u00a0IEEE International Conference on Data Mining (ICDM), pp. 1335\u20131340 (2022)","DOI":"10.1109\/ICDM54844.2022.00177"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9884-4_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T09:45:30Z","timestamp":1779615930000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9884-4_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698837","9789819698844"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9884-4_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}