We have implemented and evaluated our scheme on commercially available smartphones. More specifically, it profiles a user based on how he holds the phone and based on the characteristics of the points being pressed on the touchscreen, and not the produced signature image. The proposed mechanism takes into account micro-movements of a phone and movements of the user's finger during writing or signing on the touchscreen.
This paper presents a new, bi-modal behav-ioral biometric solution for user authentication. Most of these schemes, however, are uni-modal. As a result, several new behavioral biometric schemes have been proposed. The search for new authentication methods to replace passwords for modern mobile devices such as smartphones and tablets has attracted a substantial amount of research in recent years. Keywords Machine Learning, Behavioral Biometrics, Continuous Authentication, Mobile Devices, Attacks, Defense, Survey. Finally, our discussions extend to lessons learned, current challenges and future trends. Further, we conduct another review that showed the vulnerability of machine learning models against well-designed adversarial attack vectors and we highlight relevant countermeasures. 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. In our survey, we first present a classification of behavioral biometrics and continuous authentication technologies for mobile devices and an analysis for behavioral biometrics collection methodologies and feature extraction techniques.
Our aim is to help interested researchers to effectively grasp the background in this field and to avoid pitfalls in their work.
This paper offers an up-to-date, comprehensive, extensive and targeted survey on Behavioral Biometrics and Continuous Authentication technologies for mobile devices.
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