Lightweight anomaly detection framework for IoT

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dc.contributor.authorTagliaro Beasley, Bianca
dc.contributor.authorO'Mahony, George D.
dc.contributor.authorGómez Quintana, Sergi
dc.contributor.authorTemko, Andriy
dc.contributor.authorPopovici, Emanuel
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2022-06-23T13:55:05Z
dc.date.available2022-06-23T13:55:05Z
dc.date.issued2020-08-31
dc.date.updated2022-06-23T13:16:04Z
dc.description.abstractInternet of Things (IoT) security is growing in importance in many applications ranging from biomedical to environmental to industrial applications. Access to data is the primary target for many of these applications. Often IoT devices are an essential part of critical control systems that could affect well-being, safety, or inflict severe financial damage. No current solution addresses all security aspects. This is mainly due to the resource-constrained nature of IoT, cost, and power consumption. In this paper, we propose and analyse a framework for detecting anomalies on a low power IoT platform. By monitoring power consumption and by using machine learning techniques, we show that we can detect a large number and types of anomalies during the execution phase of an application running on the IoT. The proposed methodology is generic in nature, hence allowing for deployment in a myriad of scenarios.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationTagliaro Beasley, B., O'Mahony, G. D., Gómez Quintana, S., Temko, A. and Popovici, E. (2020) 'Lightweight anomaly detection framework for IoT', 2020 31st Irish Signals and Systems Conference (ISSC), Letterkenny, Ireland, 11-12 June. doi: 10.1109/ISSC49989.2020.9180205en
dc.identifier.doi10.1109/ISSC49989.2020.9180205en
dc.identifier.eissn2688-1454
dc.identifier.endpage6en
dc.identifier.isbn978-1-7281-9418-9
dc.identifier.isbn978-1-7281-9419-6
dc.identifier.issn2688-1446
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/13312
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2020 31st Irish Signals and Systems Conference (ISSC)
dc.rights© 2020, 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
dc.subjectAnomaly detectionen
dc.subjectARIMAen
dc.subjectEmbedded systemsen
dc.subjectIoTen
dc.subjectLow poweren
dc.subjectMachine learningen
dc.subjectSARIMAen
dc.subjectSecurityen
dc.titleLightweight anomaly detection framework for IoTen
dc.typeConference itemen
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