AuDI

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Marchal, Samuel
dc.contributor.author Miettinen, Markus
dc.contributor.author Nguyen, Thien Duc
dc.contributor.author Sadeghi, Ahmad-Reza
dc.contributor.author Asokan, N.
dc.date.accessioned 2019-09-03T13:44:54Z
dc.date.available 2019-09-03T13:44:54Z
dc.date.issued 2019-06
dc.identifier.citation Marchal , S , Miettinen , M , Nguyen , T D , Sadeghi , A-R & Asokan , N 2019 , ' AuDI : Towards Autonomous IoT Device-Type Identification using Periodic Communication ' IEEE Journal on Selected Areas in Communications , vol. 37 , no. 6 , 8664655 , pp. 1402-1412 . https://doi.org/10.1109/JSAC.2019.2904364 en
dc.identifier.issn 0733-8716
dc.identifier.issn 1558-0008
dc.identifier.other PURE UUID: 4aa54e18-407e-4086-bb6f-b0141c536d3c
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/audi(4aa54e18-407e-4086-bb6f-b0141c536d3c).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85065882414&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/32146255/AuDI_Aalto.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/40046
dc.description.abstract IoT devices are being widely deployed. But the huge variance among them in the level of security and requirements for network resources makes it infeasible to manage IoT networks using a common generic policy. One solution to this challenge is to define policies for classes of devices based on device type. In this paper, we present AUDI, a system for quickly and effectively identifying the type of a device in an IoT network by analyzing their network communications. AuDI models the periodic communication traffic of IoT devices using an unsupervised learning method to perform identification. In contrast to prior work, AuDI operates autonomously after initial setup, learning, without human intervention nor labeled data, to identify previously unseen device types. AuDI can identify the type of a device in any mode of operation or stage of lifecycle of the device. Via systematic experiments using 33 off-the-shelf IoT devices, we show that AuDI is effective (98.2% accuracy). en
dc.format.extent 11
dc.format.extent 1402-1412
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartofseries IEEE Journal on Selected Areas in Communications en
dc.relation.ispartofseries Volume 37, issue 6 en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title AuDI en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Professorship Asokan N.
dc.contributor.department Technische Universität Darmstadt
dc.contributor.department Department of Computer Science en
dc.subject.keyword Internet of Things
dc.subject.keyword device-type identification
dc.subject.keyword autonomous IoT device identification
dc.subject.keyword self-learning
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201909035088
dc.identifier.doi 10.1109/JSAC.2019.2904364
dc.type.version acceptedVersion


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