Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios

DC FieldValueLanguage
dc.contributor.authorAlmaguer-Angeles, Fernando-
dc.contributor.authorMurphy, John-
dc.contributor.authorMurphy, Liam, B.E.-
dc.contributor.authorPortillo Dominguez, Andres Omar-
dc.date.accessioned2019-07-31T08:30:41Z-
dc.date.available2019-07-31T08:30:41Z-
dc.date.copyright2019 IEEEen_US
dc.date.issued2019-04-18-
dc.identifier.urihttp://hdl.handle.net/10197/10952-
dc.description2019 IEEE 5th World Forum on Internet of Things (WF-IoT'19)2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15-18 April 2019en_US
dc.description.abstractInternet of Things (IoT) systems produce large amounts of raw data in the form of log files. This raw data must then be processed to extract useful information. Machine Learning (ML) has proved to be an efficient technique for such tasks, but there are many different ML algorithms available, each suited to different types of scenarios. In this work, we compare the performance of 22 state-of-the-art supervised ML classification algorithms on different IoT datasets, when applied to the problem of anomaly detection. Our results show that there is no dominant solution, and that for each scenario, several candidate techniques perform similarly. Based on our results and a characterization of our datasets, we propose a recommendation framework which guides practitioners towards the subset of the 22 ML algorithms which is likely to perform best on their data.en_US
dc.description.sponsorshipEuropean Commission - European Regional Development Funden_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2019 IEEE 5th World Forum on Internet of Things (WF-IoT)en_US
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectMeasurementen_US
dc.subjectInternet of Thingsen_US
dc.subjectTrainingen_US
dc.subjectAnomaly detectionen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectLibrariesen_US
dc.subjectSmart buildingsen_US
dc.titleChoosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenariosen_US
dc.typeConference Publicationen_US
dc.internal.authorcontactotherandres.portillodominguez@ucd.ieen_US
dc.internal.webversionshttp://wfiot2019.iot.ieee.org/-
dc.statusNot peer revieweden_US
dc.identifier.startpage491en_US
dc.identifier.endpage495en_US
dc.identifier.doi10.1109/wf-iot.2019.8767357-
dc.identifier.doiSFI/13/RC/2094-
dc.neeo.contributorAlmaguer-Angeles|Fernando|aut|-
dc.neeo.contributorMurphy|John|aut|-
dc.neeo.contributorMurphy|Liam, B.E.|aut|-
dc.neeo.contributorPortillo Dominguez|Andres Omar|aut|-
dc.date.updated2019-07-30T12:08:38Z-
item.fulltextWith Fulltext-
item.grantfulltextopen-
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