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Effect of attacker characterization in ECG-based continuous authentication mechanisms for Internet of Things

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: COSEC (Computer SECurity Lab)es
dc.contributor.authorPeris López, Pedro
dc.contributor.authorGonzález Manzano, Lorena
dc.contributor.authorCámara Núñez, María Carmen
dc.contributor.authorFuentes García-Romero de Tejada, José María de
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderUniversidad Carlos III de Madrides
dc.date.accessioned2020-03-18T18:43:47Z
dc.date.available2020-04-01T23:00:04Z
dc.date.issued2018-04-01
dc.description.abstractWearable devices enable retrieving data from their porting user, among other applications. When combining them with the Internet of Things (IoT) paradigm, a plethora of services can be devised. Thanks to IoT, several approaches have been proposed to apply user data, and particularly ElectroCardioGram (ECG) signals, for biometric authentication. One step further is achieving Continuous Authentication (CA), i.e., ensuring that the user remains the same during a certain period. The hardness of this task varies with the attacker characterization, that is, the amount of information about the attacker that is available to the authentication system. In this vein, we explore different ECG-based CA mechanisms for known, blind-modelled and unknown attacker settings. Our results show that, under certain configuration, 99.5 % of true positive rate can be achieved for a blind-modelled attacker, 93.5 % for a known set of attackers and 91.8 % for unknown ones.en
dc.description.sponsorshipThis work was supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You); by the CAM grant S2013/ICE-3095 (CIBER-DINE: Cybersecurity, Data, and Risks), and by the MINECO grant TIN2016-79095-C2-2-R (SMOG-DEV — Security mechanisms for fog computing: advanced security for devices). L. González and J. M. de Fuentes were also supported by the Programa de Ayudas para la Movilidad of Carlos III University of Madrid, Spain (MPP2017/MI-MA).en
dc.identifier.bibliographicCitationPeris-Lopez, P., González-Manzano L., Camara C., de Fuentes, J.M. (2018). Effect of attacker characterization in ECG-based continuous authentication mechanisms for Internet of Things . Future Generation Computer Systems, 81, pp. 67-77.es
dc.identifier.doihttps://doi.org/10.1016/j.future.2017.11.037
dc.identifier.issn0167-739X
dc.identifier.publicationfirstpage67
dc.identifier.publicationlastpage77
dc.identifier.publicationtitleFuture Generation Computer Systemen
dc.identifier.publicationvolume81
dc.identifier.urihttps://hdl.handle.net/10016/29941
dc.identifier.uxxiAR/0000024337
dc.language.isoengen
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. TIN2013-46469-Res
dc.relation.projectIDComunidad de Madrid. S2013/ICE-3095es
dc.relation.projectIDGobierno de España. TIN2016-79095-C2-2-Res
dc.relation.projectIDUniversidad Carlos III de Madrid. MPP2017/MI-MAes
dc.rights© 2017 Elsevier B.V. All rights reserved.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.otherInternet of thingsen
dc.subject.otherElectrocardiogramen
dc.subject.otherContinuous modelen
dc.subject.otherAttacker modelen
dc.titleEffect of attacker characterization in ECG-based continuous authentication mechanisms for Internet of Thingsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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