Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/22755
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Stylios, Ioannis Chr | - |
dc.contributor.author | Kokolakis, Spyros A. | - |
dc.contributor.author | Thanou, Olga | - |
dc.contributor.author | Chatzis, Sotirios P. | - |
dc.date.accessioned | 2021-06-22T11:03:02Z | - |
dc.date.available | 2021-06-22T11:03:02Z | - |
dc.date.issued | 2021-02-01 | - |
dc.identifier.citation | Information Fusion, 2021, vol. 66, pp. 76-99 | en_US |
dc.identifier.issn | 15662535 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/22755 | - |
dc.description.abstract | This paper offers an up-to-date, comprehensive, extensive and targeted survey on Behavioral Biometrics and Continuous Authentication technologies for mobile devices. Our aim is to help interested researchers to effectively grasp the background in this field and to avoid pitfalls in their work. In our survey, we first present a classification of behavioral biometrics technologies and continuous authentication for mobile devices and an analysis for behavioral biometrics collection methodologies and feature extraction techniques. Then, we provide a state-of-the-art literature review focusing on the machine learning models performance in seven types of behavioral biometrics for continuous authentication. Further, we conduct another review that showed the vulnerability of machine learning models against well-designed adversarial attack vectors and we highlight relevant countermeasures. Finally, our discussions extend to lessons learned, current challenges and future trends. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Information Fusion | en_US |
dc.rights | © Elsevier | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Machine learning | en_US |
dc.subject | Behavioral biometrics | en_US |
dc.subject | Continuous authentication | en_US |
dc.subject | Mobile devices | en_US |
dc.subject | Attacks | en_US |
dc.subject | Defense | en_US |
dc.subject | Survey | en_US |
dc.title | Behavioral biometrics & continuous user authentication on mobile devices: A survey | en_US |
dc.type | Article | en_US |
dc.collaboration | University of the Aegean | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.journals | Subscription | en_US |
dc.country | Greece | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1016/j.inffus.2020.08.021 | en_US |
dc.identifier.scopus | 2-s2.0-85090859901 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85090859901 | - |
dc.relation.volume | 66 | en_US |
cut.common.academicyear | 2020-2021 | en_US |
dc.identifier.spage | 76 | en_US |
dc.identifier.epage | 99 | en_US |
item.grantfulltext | none | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.openairetype | article | - |
item.fulltext | No Fulltext | - |
crisitem.journal.journalissn | 1566-2535 | - |
crisitem.journal.publisher | Elsevier | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-4956-4013 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
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