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dc.contributor.authorVigoya, Laura
dc.contributor.authorFernández, Diego
dc.contributor.authorCarneiro, Víctor
dc.contributor.authorCacheda, Fidel
dc.date.accessioned2020-07-31T14:30:03Z
dc.date.available2020-07-31T14:30:03Z
dc.date.issued2020-07-04
dc.identifier.citationVigoya, L.; Fernandez, D.; Carneiro, V.; Cacheda, F. Annotated Dataset for Anomaly Detection in a Data Center with IoT Sensors. Sensors 2020, 20, 3745. https://doi.org/10.3390/s20133745es_ES
dc.identifier.issn1424-8220
dc.identifier.issn1424-8239
dc.identifier.urihttp://hdl.handle.net/2183/26067
dc.description.abstract[Abstract] The relative simplicity of IoT networks extends service vulnerabilities and possibilities to different network failures exhibiting system weaknesses. Therefore, having a dataset with a sufficient number of samples, labeled and with a systematic analysis, is essential in order to understand how these networks behave and detect traffic anomalies. This work presents DAD: a complete and labeled IoT dataset containing a reproduction of certain real-world behaviors as seen from the network. To approximate the dataset to a real environment, the data were obtained from a physical data center, with temperature sensors based on NFC smart passive sensor technology. Having carried out different approaches, performing mathematical modeling using time series was finally chosen. The virtual infrastructure necessary for the creation of the dataset is formed by five virtual machines, a MQTT broker and four client nodes, each of them with four sensors of the refrigeration units connected to the internal IoT network. DAD presents a seven day network activity with three types of anomalies: duplication, interception and modification on the MQTT message, spread over 5 days. Finally, a feature description is performed, so it can be used for the application of the various techniques of prediction or automatic classification.es_ES
dc.description.sponsorshipThis project was funded by the Accreditation, Structuring, and Improvement of Consolidated Research Units and Singular Centers (ED431G/01), funded by Vocational Training of the Xunta de Galicia endowed with EU FEDER funds. This research was partially supported by the Ministry of Science and Innovation, Spain’s National Research and Development Plan, through the PID2019-111388GB-I00 projectes_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.language.isoenges_ES
dc.publisherMDPI AGes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111388GB-I00/ES/DETECCION TEMPRANA DE INTRUSIONES Y ANOMALIAS EN REDES DEFINIDAS POR SOFTWARE/
dc.relation.urihttps://doi.org/10.3390/s20133745es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectDatasetes_ES
dc.subjectIoTes_ES
dc.subjectSensorses_ES
dc.titleAnnotated Dataset for Anomaly Detection in a Data Center with IoT Sensorses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleSensorses_ES
UDC.volume20es_ES
UDC.issue13es_ES
UDC.startPage3745es_ES
dc.identifier.doi10.3390/s20133745


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