Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/5573
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bıçakcı, Kemal | - |
dc.contributor.author | Salman, O. | - |
dc.contributor.author | Uzunay Y. | - |
dc.contributor.author | Tan, M. | - |
dc.date.accessioned | 2021-09-11T15:19:16Z | - |
dc.date.available | 2021-09-11T15:19:16Z | - |
dc.date.issued | 2020 | en_US |
dc.identifier.citation | 13th International Conference on Information Security and Cryptology, ISCTURKEY 2020, 3 December 2020 through 4 December 2020, , 166977 | en_US |
dc.identifier.isbn | 9781665418638 | - |
dc.identifier.uri | https://doi.org/10.1109/ISCTURKEY51113.2020.9307967 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/5573 | - |
dc.description.abstract | The 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.sponsorship | Türkiye Bilimsel ve Teknolojik Araştirma Kurumu: 118E399 | en_US |
dc.description.sponsorship | Aselsan;Havelsan;Huawei;NETAS;TURKSAT | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | 2020 International Conference on Information Security and Cryptology, ISCTURKEY 2020 - Proceedings | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Anomaly Detection | en_US |
dc.subject | Behavioural Biometrics | en_US |
dc.subject | Contextual Authentication | en_US |
dc.subject | Keystroke Dynamics | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | User Authentication | en_US |
dc.title | Analysis and Evaluation of Keystroke Dynamics as a Feature of Contextual Authentication | en_US |
dc.type | Conference Object | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.identifier.startpage | 11 | en_US |
dc.identifier.endpage | 17 | en_US |
dc.identifier.wos | WOS:000676395800003 | en_US |
dc.identifier.scopus | 2-s2.0-85101145878 | en_US |
dc.institutionauthor | Bıçakcı, Kemal | - |
dc.institutionauthor | Tan, Mehmet | - |
dc.identifier.doi | 10.1109/ISCTURKEY51113.2020.9307967 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.relation.conference | 13th International Conference on Information Security and Cryptology, ISCTURKEY 2020 | en_US |
item.openairetype | Conference Object | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.3. Department of Computer Engineering | - |
crisitem.author.dept | 02.1. Department of Artificial Intelligence 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 |
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