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|Title:||Behavioral biometrics & continuous user authentication on mobile devices: A survey||Authors:||Stylios, Ioannis Chr
Kokolakis, Spyros A.
Chatzis, Sotirios P.
|Major Field of Science:||Natural Sciences||Field Category:||Computer and Information Sciences||Keywords:||Machine learning;Behavioral biometrics;Continuous authentication;Mobile devices;Attacks;Defense;Survey||Issue Date:||1-Feb-2021||Source:||Information Fusion, 2021, vol. 66, pp. 76-99||Volume:||66||Start page:||76||End page:||99||Journal:||Information Fusion||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.||URI:||https://ktisis.cut.ac.cy/handle/10488/22755||ISSN:||1566-2535||DOI:||10.1016/j.inffus.2020.08.021||Rights:||© Elsevier
Attribution-NonCommercial-NoDerivatives 4.0 International
|Type:||Article||Affiliation :||University of the Aegean
Cyprus University of Technology
|Appears in Collections:||Άρθρα/Articles|
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checked on Sep 21, 2021
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