{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T11:51:18Z","timestamp":1742989878362,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031545276"},{"type":"electronic","value":"9783031545283"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-54528-3_21","type":"book-chapter","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T07:06:29Z","timestamp":1708585589000},"page":"375-391","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Semi-supervised IoT Time Series Anomaly Detection Model Using Graph Structure Learning"],"prefix":"10.1007","author":[{"given":"Weijian","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunni","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghui","family":"Xi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxia","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"issue":"2","key":"21_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang, G., et al.: Deep learning for anomaly detection: a review. ACM Comput. Surv. (CSUR) 54(2), 1\u201338 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Sharma, B., Sharma, L., Lal, C.: Anomaly detection techniques using deep learning in IoT: a survey. In: 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 146\u2013149. IEEE (2019)","DOI":"10.1109\/ICCIKE47802.2019.9004362"},{"issue":"17","key":"21_CR3","doi-asserted-by":"publisher","first-page":"4784","DOI":"10.1002\/cpe.3462","volume":"27","author":"P Chen","year":"2015","unstructured":"Chen, P., et al.: A probabilistic model for performance analysis of cloud infrastructures. Concurr. Comput. Pract. Exp. 27(17), 4784\u20134796 (2015)","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"21_CR4","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1007\/s11036-019-01450-0","volume":"25","author":"Y Pan","year":"2020","unstructured":"Pan, Y., et al.: A novel approach to scheduling workflows upon cloud resources with fluctuating performance. Mob. Netw. Appl. 25, 690\u2013700 (2020)","journal-title":"Mob. Netw. Appl."},{"key":"21_CR5","unstructured":"Tukey, J.W.: Exploratory Data Analysis, vol. 2 (1977)"},{"key":"21_CR6","unstructured":"van den Oord, A., et al.: WaveNet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)"},{"key":"21_CR7","unstructured":"Filonov, P., Lavrentyev, A., Vorontsov, A.: Multivariate industrial time series with cyber-attack simulation: fault detection using an LSTM-based predictive data model. arXiv preprint arXiv:1612.06676 (2016)"},{"key":"21_CR8","unstructured":"Bodin, E., et al.: Nonparametric inference for auto-encoding variational Bayes. arXiv preprint arXiv:1712.06536 (2017)"},{"issue":"11","key":"21_CR9","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"21_CR10","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"21_CR11","unstructured":"Veli\u010dkovi\u0107, P., et al.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"21_CR12","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"21_CR13","unstructured":"Braei, M., Wagner, S.: Anomaly detection in univariate time-series: a survey on the state-of-the-art. arXiv preprint arXiv:2004.00433 (2020)"},{"issue":"2","key":"21_CR14","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.jbankfin.2007.03.009","volume":"32","author":"TG Bali","year":"2008","unstructured":"Bali, T.G., Mo, H., Tang, Y.: The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR. J. Bank. Financ. 32(2), 269\u2013282 (2008)","journal-title":"J. Bank. Financ."},{"key":"21_CR15","unstructured":"Chandola, V.: Anomaly detection for symbolic sequences and time series data. University of Minnesota (2009)"},{"key":"21_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/3-540-45681-3_2","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"F Angiulli","year":"2002","unstructured":"Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS, vol. 2431, pp. 15\u201327. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45681-3_2"},{"key":"21_CR17","unstructured":"Shyu, M.-L., et al.: A novel anomaly detection scheme based on principal component classifier. In: Proceedings of the IEEE Foundations and New Directions of Data Mining Workshop, pp. 172\u2013179. IEEE Press (2003)"},{"key":"21_CR18","unstructured":"Li, Y., et al.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining. IEEE (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"21_CR20","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"21_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1007\/978-3-030-30490-4_56","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Text and Time Series","author":"D Li","year":"2019","unstructured":"Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.-K.: MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: Tetko, I.V., K\u016frkov\u00e1, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 703\u2013716. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30490-4_56"},{"issue":"11","key":"21_CR22","doi-asserted-by":"publisher","first-page":"2909","DOI":"10.1093\/comjnl\/bxac085","volume":"65","author":"P Chen","year":"2022","unstructured":"Chen, P., et al.: Effectively detecting operational anomalies in large-scale IoT data infrastructures by using a GAN-based predictive model. Comput. J. 65(11), 2909\u20132925 (2022)","journal-title":"Comput. J."},{"key":"21_CR23","first-page":"1","volume":"80","author":"S Qi","year":"2023","unstructured":"Qi, S., et al.: An efficient GAN-based predictive framework for multivariate time series anomaly prediction in cloud data centers. J. Supercomput. 80, 1\u201326 (2023)","journal-title":"J. Supercomput."},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Audibert, J., et al.: USAD: unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3395\u20133404 (2020)","DOI":"10.1145\/3394486.3403392"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Su, Y., et al.: Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2828\u20132837 (2019)","DOI":"10.1145\/3292500.3330672"},{"key":"21_CR26","unstructured":"Nicolicioiu, A., Duta, I., Leordeanu, M.: Recurrent space-time graph neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5 (2021)","DOI":"10.1609\/aaai.v35i5.16523"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Wu, Z., et al.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 753\u2013763 (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"21_CR29","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in neural Information Processing Systems, vol. 30 (2017)"},{"key":"21_CR30","unstructured":"Liu, Z., et al.: Rethinking the value of network pruning. arXiv preprint arXiv:1810.05270 (2018)"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Vu, Q.H., et al.: A graph method for keyword-based selection of the top-k databases. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 915\u2013926 (2008)","DOI":"10.1145\/1376616.1376707"},{"key":"21_CR32","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s00591-010-0080-8","volume":"58","author":"F Klinker","year":"2011","unstructured":"Klinker, F.: Exponential moving average versus moving exponential average. Math. Semesterber. 58, 97\u2013107 (2011)","journal-title":"Math. Semesterber."},{"key":"21_CR33","doi-asserted-by":"crossref","unstructured":"Mathur, A.P., Tippenhauer, N.O.: SWaT: a water treatment testbed for research and training on ICS security. In: 2016 International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater), pp. 31\u201336. IEEE (2016)","DOI":"10.1109\/CySWater.2016.7469060"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Collaborative Computing: Networking, Applications and Worksharing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-54528-3_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T07:29:28Z","timestamp":1708586968000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-54528-3_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031545276","9783031545283"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-54528-3_21","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CollaborateCom","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Collaborative Computing: Networking, Applications and Worksharing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"colcom2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Cony +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"176","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}