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Adaptive Real-Time Method for Anomaly Detection Using Machine Learning
dc.contributor.author | Novoa-Paradela, David | |
dc.contributor.author | Fontenla-Romero, Óscar | |
dc.contributor.author | Guijarro-Berdiñas, Bertha | |
dc.date.accessioned | 2020-10-08T14:43:58Z | |
dc.date.available | 2020-10-08T14:43:58Z | |
dc.date.issued | 2020-08-20 | |
dc.identifier.citation | Novoa-Paradela, D.; Fontenla-Romero, Ó.; Guijarro-Berdiñas, B. Adaptive Real-Time Method for Anomaly Detection Using Machine Learning. Proceedings 2020, 54, 38. https://doi.org/10.3390/proceedings2020054038 | es_ES |
dc.identifier.issn | 2504-3900 | |
dc.identifier.uri | http://hdl.handle.net/2183/26380 | |
dc.description.abstract | [Abstract] Anomaly detection is a sub-area of machine learning that deals with the development of methods to distinguish among normal and anomalous data. Due to the frequent use of anomaly-detection systems in monitoring and the lack of methods capable of learning in real time, this research presents a new method that provides such online adaptability. The method bases its operation on the properties of scaled convex hulls. It begins building a convex hull, using a minimum set of data, that is adapted and subdivided along time to accurately fit the boundary of the normal class data. The model has online learning ability and its execution can be carried out in a distributed and parallel way, all of them interesting advantages when dealing with big datasets. The method has been compared to other state-of-the-art algorithms demonstrating its effectiveness. | es_ES |
dc.description.sponsorship | This work has been supported by Spanish Government’s Secretaría de Estado de Investigación (Grant TIN2015-65069-C2-1-R), Xunta de Galicia (Grants ED431C 2018/34 and ED431G/01) and EU FEDER funds. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2018/34 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2015-65069-C2-1-R/ES/ALGORITMOS ESCALABLES DE APRENDIZAJE COMPUTACIONAL: MAS ALLA DE LA CLASIFICACION Y LA REGRESION | |
dc.relation.uri | https://doi.org/10.3390/proceedings2020054038 | es_ES |
dc.rights | Atribución 4.0 Internacional (CC BY 4.0) | es_ES |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Anomaly detection | es_ES |
dc.subject | Convex hull | es_ES |
dc.subject | Data streaming | es_ES |
dc.subject | Big data | es_ES |
dc.title | Adaptive Real-Time Method for Anomaly Detection Using Machine Learning | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Proceedings | es_ES |
UDC.volume | 54 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 38 | es_ES |
dc.identifier.doi | 10.3390/proceedings2020054038 | |
UDC.conferenceTitle | 3rd XoveTIC Conference; A Coruña, Spain; 8–9 October 2020 | es_ES |