dc.contributor.author | Ivannikova, Elena | |
dc.contributor.author | David, Gil | |
dc.contributor.author | Hämäläinen, Timo | |
dc.date.accessioned | 2017-12-13T12:31:53Z | |
dc.date.available | 2017-12-13T12:31:53Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Ivannikova, E., David, G., & Hämäläinen, T. (2017). Anomaly detection approach to keystroke dynamics based user authentication. In <i>ISCC 2017 : Proceedings of the 2017 IEEE Symposium on Computers and Communications</i> (pp. 885-889). IEEE. Proceedings : IEEE Symposium on Computers and Communications. <a href="https://doi.org/10.1109/ISCC.2017.8024638" target="_blank">https://doi.org/10.1109/ISCC.2017.8024638</a> | |
dc.identifier.other | CONVID_27298398 | |
dc.identifier.other | TUTKAID_75404 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/56305 | |
dc.description.abstract | Keystroke dynamics is one of the authentication mechanisms which uses natural typing pattern of a user for identification. In this work, we introduced Dependence Clustering based approach to user authentication using keystroke dynamics. In addition, we applied a k-NN-based approach that demonstrated strong results. Most of the existing approaches use only genuine users data for training and validation. We designed a cross validation procedure with artificially generated impostor samples that improves the learning process yet allows fair comparison to previous works. We evaluated the methods using the CMU keystroke dynamics benchmark dataset. Both proposed approaches outperformed the previous state-of-the-art results for the CMU dataset for unsupervised learning. | |
dc.format.extent | 1385 | |
dc.language.iso | eng | |
dc.publisher | IEEE | |
dc.relation.ispartof | ISCC 2017 : Proceedings of the 2017 IEEE Symposium on Computers and Communications | |
dc.relation.ispartofseries | Proceedings : IEEE Symposium on Computers and Communications | |
dc.subject.other | tietoturva | |
dc.subject.other | pääsynvalvonta | |
dc.subject.other | todentaminen | |
dc.subject.other | data security | |
dc.subject.other | access control | |
dc.subject.other | authentication | |
dc.title | Anomaly detection approach to keystroke dynamics based user authentication | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-201712134659 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.date.updated | 2017-12-13T10:15:10Z | |
dc.relation.isbn | 978-1-5386-1629-1 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 885-889 | |
dc.relation.issn | 1530-1346 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © 2017 IEEE. This is a final draft of an article whose final and definitive version has been published by 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. | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.conference | IEEE Symposium on Computers and Communications | |
dc.subject.yso | tietoturva | |
dc.subject.yso | pääsynvalvonta | |
dc.subject.yso | todentaminen | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p5479 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p15780 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p24130 | |
dc.relation.doi | 10.1109/ISCC.2017.8024638 | |
dc.type.okm | A4 | |