{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T16:29:23Z","timestamp":1791563363835,"version":"4.3.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031217425","type":"print"},{"value":"9783031217432","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-21743-2_24","type":"book-chapter","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T14:24:05Z","timestamp":1670509445000},"page":"299-312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Using Deep Learning to\u00a0Detect Anomalies in\u00a0Traffic Flow"],"prefix":"10.1007","author":[{"given":"Manuel","family":"M\u00e9ndez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfredo","family":"Ibias","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9808-6401","authenticated-orcid":false,"given":"Manuel","family":"N\u00fa\u00f1ez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Alam, Md R., Gerostathopoulos, I., Amini, S., Prehofer, C., Attanasi, A.: Adaptable anomaly detection in traffic flow time series. In: 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 1\u20139 (2019)","DOI":"10.1109\/MTITS.2019.8883338"},{"key":"24_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csi.2020.103426","volume":"70","author":"D Corral-Plaza","year":"2020","unstructured":"Corral-Plaza, D., Medina-Bulo, I., Ortiz, G., Boubeta-Puig, J.: A stream processing architecture for heterogeneous data sources in the internet of things. Comput. Standard. Interfaces 70, 103426 (2020)","journal-title":"Comput. Standard. Interfaces"},{"key":"24_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106682","volume":"213","author":"D Corral-Plaza","year":"2021","unstructured":"Corral-Plaza, D., Ortiz, G., Medina-Bulo, I., Boubeta-Puig, J.: MEdit4CEP-SP: a model-driven solution to improve decision-making through user-friendly management and real-time processing of heterogeneous data streams. Knowl. Based Syst. 213, 106682 (2021)","journal-title":"Knowl. Based Syst."},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Ding, N., Ma, H., Gao, H., Ma, H., Tan, G.: Real-time anomaly detection based on long short-term memory and gaussian mixture model. Comput. Electr. Eng. 79, 106458, (2019)","DOI":"10.1016\/j.compeleceng.2019.106458"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Djenouri, Y., Belhadi, A., Lin, J. C.-W., Cano, A.: Adapted k-nearest neighbors for detecting anomalies on spatio-temporal traffic flow. IEEE Access 7, 10015\u201310027 (2019)","DOI":"10.1109\/ACCESS.2019.2891933"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Djenouri, Y., Belhadi, A., Lin, J. C.-W., Djenouri, D., Cano, A.: A survey on urban traffic anomalies detection algorithms. IEEE Access 7, 12192\u201312205 (2019)","DOI":"10.1109\/ACCESS.2019.2893124"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Djenouri, Y., Zimek, A., Chiarandini, M.: Outlier detection in urban traffic flow distributions. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 935\u2013940 (2018)","DOI":"10.1109\/ICDM.2018.00114"},{"key":"24_CR8","unstructured":"Gugulothu, N., Malhotra, P., Vig, L., Shroff, G.: Sparse neural networks for anomaly detection in high-dimensional time series (2018)"},{"issue":"8","key":"24_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Hong, Y., Wang, L., Kang, J., Wang, H., Gao, Z.: A novel application approach for anomaly detection and fault determination process based on machine learning. In: 2020 6th International Symposium on System and Software Reliability (ISSSR), pp. 1\u20135 (2020)","DOI":"10.1109\/ISSSR51244.2020.00009"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Hsieh, R.-J., Chou, J., Ho, C.-H.: Unsupervised online anomaly detection on multivariate sensing time series data for smart manufacturing, pp. 90\u201397 (2019)","DOI":"10.1109\/SOCA.2019.00021"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Hundman, K., Constantinou, V., Laporte, C., Colwell, I., S\u00f6derstr\u00f6m, T.: Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding. CoRR, abs\/1802.04431 (2018)","DOI":"10.1145\/3219819.3219845"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, J., Chen, J., Gu, T., Raymond Choo, K.-K., Liu, C., Yu, M., Huang, W., Mohapatra, P.: Anomaly detection with graph convolutional networks for insider threat and fraud detection. In: MILCOM 2019\u20132019 IEEE Military Communications Conference (MILCOM), pp. 109\u2013114 (2019)","DOI":"10.1109\/MILCOM47813.2019.9020760"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Karthik, S., Supreetha, H.V., Sandhya, S.: Detection of anomalies in time series data. In: 2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), pp. 1\u20135 (2021)","DOI":"10.1109\/CSITSS54238.2021.9683715"},{"key":"24_CR15","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-658-19287-7_13","volume-title":"Data Science \u2013 Analytics and Applications","author":"M Atzmueller","year":"2017","unstructured":"Atzmueller, M., Arnu, D., Schmidt, A.: Anomaly detection and structural analysis in industrial production environments. In: Data Science \u2013 Analytics and Applications, pp. 91\u201395. Springer, Wiesbaden (2017). https:\/\/doi.org\/10.1007\/978-3-658-19287-7_13"},{"key":"24_CR16","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.ins.2014.02.114","volume":"277","author":"C Laorden","year":"2014","unstructured":"Laorden, C., Ugarte-Pedrero, X., Santos, I., Sanz, B., Nieves, J., Bringas, P.G.: Study on the effectiveness of anomaly detection for spam filtering. Inf. Sci. 277, 421\u2013444 (2014)","journal-title":"Inf. Sci."},{"issue":"4","key":"24_CR17","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989)","journal-title":"Neural Comput."},{"key":"24_CR18","doi-asserted-by":"publisher","first-page":"40281","DOI":"10.1109\/ACCESS.2018.2851747","volume":"6","author":"Y Li","year":"2018","unstructured":"Li, Y., Guo, T., Xia, R., Xie, W.: Road traffic anomaly detection based on fuzzy theory. IEEE Access 6, 40281\u201340288 (2018)","journal-title":"IEEE Access"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Rajeswari, A.M., Yalini, S.K., Janani, R., Rajeswari, N., Rajeswari, N., Deisy, C.: A comparative evaluation of supervised and unsupervised methods for detecting outliers. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1068\u20131073 (2018)","DOI":"10.1109\/ICICCT.2018.8473123"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Ripan, R.C., Sarker, I.H., Furhad, M.H., Anwar,M.M., Hoque, M.M: An effective heart disease prediction model based on machine learning techniques. https:\/\/www.preprints.org\/manuscript\/202011.0744\/ (2020)","DOI":"10.20944\/preprints202011.0744.v1"},{"key":"24_CR21","unstructured":"Shen, L., Li, Z., Kwok, J.: Timeseries anomaly detection using temporal hierarchical one-class network. In: H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, In: Advances in Neural Information Processing Systems vol. 33, pp. 13016\u201313026 Curran Associates Inc (2020)"},{"key":"24_CR22","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: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD \u201919, pp. 2828\u20132837, New York, NY, USA, (2019). Association for Computing Machinery","DOI":"10.1145\/3292500.3330672"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Ti\u0161ljari\u0107, L., Fernandes, S., Cari\u0107, T., Gama, J.: Spatiotemporal road traffic anomaly detection: a tensor-based approach. Appl. Sci. 11(24), 12017 (2021)","DOI":"10.3390\/app112412017"},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Werra, L.V., Tunstall, L., Hofer, S.: Unsupervised anomaly detection for seasonal time series. In: 2019 6th Swiss Conference on Data Science (SDS), pp. 136\u2013137 (2019)","DOI":"10.1109\/SDS.2019.00036"}],"container-title":["Lecture Notes in Computer Science","Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21743-2_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T02:44:55Z","timestamp":1728528295000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21743-2_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031217425","9783031217432"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21743-2_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"9 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}