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Choosing machine learning algorithms for anomaly dection in smart builidng Iot scenarios

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dc.contributor.author Portillo-Domínguez, Andrés Omar
dc.contributor.author Murphy, John
dc.contributor.author Murphy, Liam
dc.contributor.author Portillo-Dominguez, Omar A.
dc.date.accessioned 2020-01-22T11:14:53Z
dc.date.available 2020-01-22T11:14:53Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/10344/8408
dc.description peer-reviewed en_US
dc.description.abstract Internet 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.language.iso eng en_US
dc.publisher IEEE Computer Society en_US
dc.relation.ispartofseries 2019 IEEE 5th World Forum on Internet of Things (WF-IoT);
dc.relation.uri http://dx.doi.org/10.1109/WF-IoT.2019.8767357
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.subject IoT en_US
dc.subject software engineering en_US
dc.title Choosing machine learning algorithms for anomaly dection in smart builidng Iot scenarios en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1109/WF-IoT.2019.8767357
dc.contributor.sponsor SFI en_US
dc.relation.projectid 13/RC/2094 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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