Publication:
Can a smartband be used for continuous implicit authentication in real life

dc.contributor.coauthorEkiz, Deniz
dc.contributor.coauthorDardağan Yağmur Ceren
dc.contributor.coauthorErsoy, Cem
dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.kuauthorCan, Yekta Said
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2024-11-09T13:09:26Z
dc.date.issued2020
dc.description.abstractThe use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution, which will make these systems less vulnerable to the spoofing attacks. Physiological signals can be used for continuous authentication due to their uniqueness. Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors, which enable us to extract heart rate variability (HRV) features. In this study, we show that these devices can be used for continuous physiological authentication for enhancing the security of the cloud, edge services, and IoT devices. A system that is suitable for the smartband framework comes with new challenges such as relatively low signal quality and artifacts due to placement, which were not encountered in full lead electrocardiogram systems. After the artifact removal, cleaned physiological signals are fed to the machine learning algorithms. In order to train our machine learning models, we collected physiological data using off-the-shelf smartbands and smartwatches in a real-life event. By applying a minimum quality threshold, we achieved a 3.96% Equal Error Rate. Performance evaluation shows that HRV is a strong candidate for continuous unobtrusive implicit physiological authentication.
dc.description.fulltextYES
dc.description.indexedbyScopus
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipTurkish Directorate of Strategy and Budget, TAM Project
dc.description.versionPublisher version
dc.description.volume8
dc.identifier.doi10.1109/ACCESS.2020.2982852
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR02175
dc.identifier.issn2169-3536
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85083072875
dc.identifier.urihttps://hdl.handle.net/20.500.14288/2756
dc.keywordsContinuous authentication
dc.keywordsHeart rate variability
dc.keywordsSmartband
dc.keywordsSmartwatch
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.grantno2007K12-873
dc.relation.ispartofIEEE Access
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/8819
dc.subjectAuthentication
dc.subjectMachine learning
dc.titleCan a smartband be used for continuous implicit authentication in real life
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorCan, Yekta Said
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Computer Engineering
relation.isOrgUnitOfPublication89352e43-bf09-4ef4-82f6-6f9d0174ebae
relation.isOrgUnitOfPublication.latestForDiscovery89352e43-bf09-4ef4-82f6-6f9d0174ebae
relation.isParentOrgUnitOfPublication8e756b23-2d4a-4ce8-b1b3-62c794a8c164
relation.isParentOrgUnitOfPublication.latestForDiscovery8e756b23-2d4a-4ce8-b1b3-62c794a8c164

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
8819.pdf
Size:
1.65 MB
Format:
Adobe Portable Document Format