期刊
INFORMATION FUSION
卷 66, 期 -, 页码 76-99出版社
ELSEVIER
DOI: 10.1016/j.inffus.2020.08.021
关键词
Machine Learning; Behavioral Biometrics; Continuous Authentication; Mobile Devices; Attacks; Defense; Survey
资金
- General Secretariat for Research and Technology (GSRT)
- Hellenic Foundation for Research and Innovation (HFRI) [13_PR_2929-1]
This paper provides a comprehensive survey and review on Behavioral Biometrics and Continuous Authentication technologies for mobile devices, covering classification, feature extraction, machine learning model performance, vulnerability to adversarial attacks, and countermeasures.
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.
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