Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5573
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBıçakcı, Kemal-
dc.contributor.authorSalman, O.-
dc.contributor.authorUzunay Y.-
dc.contributor.authorTan, M.-
dc.date.accessioned2021-09-11T15:19:16Z-
dc.date.available2021-09-11T15:19:16Z-
dc.date.issued2020en_US
dc.identifier.citation13th International Conference on Information Security and Cryptology, ISCTURKEY 2020, 3 December 2020 through 4 December 2020, , 166977en_US
dc.identifier.isbn9781665418638-
dc.identifier.urihttps://doi.org/10.1109/ISCTURKEY51113.2020.9307967-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/5573-
dc.description.abstractThe current best practice dictates that even when the correct username and password are entered, the system should look for login anomalies that might indicate malicious attempts. Most anomaly detection approaches examine static properties of user's contextual data such as IP address, screen size and browser type. Keystroke Dynamics bring additional security measure and enable us to use individuals' keystroke behaviour to decide legitimacy of the user. In this paper, we first analyze different anomaly detection approaches separately and then show accuracy improvements when we combine these solutions with various methods. Our results show that including keystroke dynamics scores in session context anomaly component as a new feature performs better than ensemble methods with different weights for session context and keystroke dynamics components. We argue that this is due to the opportunity to capture the behavioral deviations of the individuals in our augmented model. © 2020 IEEE.en_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştirma Kurumu: 118E399en_US
dc.description.sponsorshipAselsan;Havelsan;Huawei;NETAS;TURKSATen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2020 International Conference on Information Security and Cryptology, ISCTURKEY 2020 - Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAnomaly Detectionen_US
dc.subjectBehavioural Biometricsen_US
dc.subjectContextual Authenticationen_US
dc.subjectKeystroke Dynamicsen_US
dc.subjectMachine Learningen_US
dc.subjectUser Authenticationen_US
dc.titleAnalysis and Evaluation of Keystroke Dynamics as a Feature of Contextual Authenticationen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage11en_US
dc.identifier.endpage17en_US
dc.identifier.wosWOS:000676395800003en_US
dc.identifier.scopus2-s2.0-85101145878en_US
dc.institutionauthorBıçakcı, Kemal-
dc.institutionauthorTan, Mehmet-
dc.identifier.doi10.1109/ISCTURKEY51113.2020.9307967-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference13th International Conference on Information Security and Cryptology, ISCTURKEY 2020en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

1
checked on Mar 23, 2024

WEB OF SCIENCETM
Citations

2
checked on Jan 20, 2024

Page view(s)

60
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.