{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T21:03:44Z","timestamp":1768338224380,"version":"3.49.0"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030134686","type":"print"},{"value":"9783030134693","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-13469-3_28","type":"book-chapter","created":{"date-parts":[[2019,3,2]],"date-time":"2019-03-02T13:03:53Z","timestamp":1551531833000},"page":"237-244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Computing Anomaly Score Threshold with Autoencoders Pipeline"],"prefix":"10.1007","author":[{"given":"Igr Alex\u00e1nder","family":"Fern\u00e1ndez-Sa\u00faco","sequence":"first","affiliation":[]},{"given":"Niusvel","family":"Acosta-Mendoza","sequence":"additional","affiliation":[]},{"given":"Andr\u00e9s","family":"Gago-Alonso","sequence":"additional","affiliation":[]},{"given":"Edel Bartolo","family":"Garc\u00eda-Reyes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,3]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.3233\/IDA-163137","volume":"21","author":"V Herrera-Semenets","year":"2017","unstructured":"Herrera-Semenets, V., P\u00e9rez Garc\u00eda, O.A., Gago-Alonso, A., Hern\u00e1ndez-Le\u00f3n, R.: Classification rule-based models for malicious activity detection. Intell. Data Anal. 21, 1141\u20131154 (2017)","journal-title":"Intell. Data Anal."},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Denning, D.E.: An intrusion-detection model. IEEE Trans. Softw. Eng. SE-13(2), 222\u2013232 (1987)","DOI":"10.1109\/TSE.1987.232894"},{"key":"28_CR3","unstructured":"Jones, A.K., Sielken, R.S.: Computer system intrusion detection: a survey. Technical report, University of Virginia, February 2001"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. CoRR abs\/1802.09089 (2018)","DOI":"10.14722\/ndss.2018.23204"},{"key":"28_CR5","first-page":"18","volume":"9","author":"A Pumsirirat","year":"2018","unstructured":"Pumsirirat, A., Yan, L.: Credit card fraud detection using deep learning based on auto-encoder and restricted Boltzmann machine. Int. J. Adv. Comput. Sci. Appl. 9, 18\u201325 (2018)","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"28_CR6","unstructured":"Schreyer, M., Sattarov, T., Borth, D., Dengel, A., Reimer, B.: Detection of anomalies in large scale accounting data using deep autoencoder networks. CoRR abs\/1709.05254 (2017)"},{"issue":"11","key":"28_CR7","doi-asserted-by":"publisher","first-page":"13173","DOI":"10.1007\/s11042-017-4940-2","volume":"77","author":"MG Narasimhan","year":"2018","unstructured":"Narasimhan, M.G., Sowmya Kamath, S.: Dynamic video anomaly detection and localization using sparse denoising autoencoders. Multimed. Tools Appl. 77(11), 13173\u201313195 (2018)","journal-title":"Multimed. Tools Appl."},{"key":"28_CR8","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.cviu.2016.10.010","volume":"156","author":"D Xu","year":"2017","unstructured":"Xu, D., Yan, Y., Ricci, E., Sebe, N.: Detecting anomalous events in videos by learning deep representations of appearance and motion. Comput. Vis. Image Underst. 156, 117\u2013127 (2017). Image and Video Understanding in Big Data","journal-title":"Comput. Vis. Image Underst."},{"key":"28_CR9","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.neunet.2018.02.015","volume":"102","author":"YJ Zheng","year":"2018","unstructured":"Zheng, Y.J., Zhou, X.H., Sheng, W.G., Xue, Y., Chen, S.Y.: Generative adversarial network based telecom fraud detection at the receiving bank. Neural Networks 102, 78\u201386 (2018)","journal-title":"Neural Networks"},{"key":"28_CR10","volume-title":"Deep Learning: A Practitioner\u2019s Approach","author":"J Patterson","year":"2017","unstructured":"Patterson, J., Gibson, A.: Deep Learning: A Practitioner\u2019s Approach. O\u2019Reilly, Beijing (2017)"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. CoRR abs\/1802.03903 (2018)","DOI":"10.1145\/3178876.3185996"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Hawkins, S., He, H., Williams, G., Baxter, R.: Outlier detection using replicator neural networks. In: Proceedings of the Fifth International Conference and Data Warehousing and Knowledge Discovery (DaWaK02), pp. 170\u2013180 (2002)","DOI":"10.1007\/3-540-46145-0_17"},{"key":"28_CR13","volume-title":"Variational Autoencoder Based Anomaly Detection Using Reconstruction Probability","author":"J An","year":"2015","unstructured":"An, J., Cho, S.: Variational Autoencoder Based Anomaly Detection Using Reconstruction Probability. Seoul National University, Seoul (2015)"},{"key":"28_CR14","doi-asserted-by":"publisher","DOI":"10.1093\/acref\/9780199541454.001.0001","volume-title":"A Dictionary of Statistics","author":"G Upton","year":"2008","unstructured":"Upton, G., Cook, I.: A Dictionary of Statistics. Oxford University Press, New York (2008)"},{"key":"28_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/4184196","volume":"2017","author":"Y Yu","year":"2017","unstructured":"Yu, Y., Long, J., Cai, Z.: Network intrusion detection through stacking dilated convolutional autoencoders. Secur. Commun. Networks 2017, 1\u201310 (2017)","journal-title":"Secur. Commun. Networks"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Gower, J.C., Ross, G.J.S.: Minimum spanning trees and single linkage cluster analysis. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 18(1), 54\u201364 (1969)","DOI":"10.2307\/2346439"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-13469-3_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T01:06:00Z","timestamp":1677719160000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-13469-3_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030134686","9783030134693"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-13469-3_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/atvs.ii.uam.es\/ciarp2018\/","order":11,"name":"conference_url","label":"Conference URL","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"187","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":"112","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":"60% - 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":"2,94","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":"5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}