A behavioral biometric challenge and response approach to user authentication on smartphones

Burgbacher, Ulrich; Prätorius, Manuel; Hinrichs, Klaus

doi:10.1109/smc.2014.6974441

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Challenges can vary in degree in which they are noticeable by a person. This can range from fully covert, e.g., ultrasonic sounds causing fine vibrations in the lips while speaking [16], partially covert, e.g., determining ear canal structure by playing sounds into a person's ear [17], or very overt, e.g., in the patterns drawn with a finger on a smartphone [18]. Noticeability of a challenge can have a big impact on user experience (UX).…”

Section: Noticeability Covert To Overtmentioning

confidence: 99%

Presentation attack detection and biometric recognition in a challenge-response formalism

Haasnoot

Spreeuwers

Veldhuis

2022

EURASIP J. on Info. Security

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Presentation attack detection (PAD) is used to mitigate the dangers of the weakest link problem in biometric recognition, in which failure modes of one application affect the security of all other applications. Strong PAD methods are therefore a must, and we believe biometric challenge-response protocols (BCRP) form an underestimated part of this ecosystem. In this paper, we conceptualize what BCRPs are, and we propose a descriptive formalism and categorization for working with them. We validate the categorization against existing literature that we classified to be describing BCRPs. Lastly, we discuss how strong BCRPs provide advantages over PAD methods, specifically in the protection of individual applications and the protection of other applications from inadvertent leaks in BCRP applications. We note that research in BCRPs is fragmented, and our intent for the proposed formalism and categorization are to give focus and direction to research efforts into biometric challenge-response protocols.

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Section: Noticeability Covert To Overtmentioning

confidence: 99%

Presentation attack detection and biometric recognition in a challenge-response formalism

Haasnoot

Spreeuwers

Veldhuis

2022

EURASIP J. on Info. Security

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“…To eliminate any sensor noise and outliers, they divided the curves into small segments in a pre-processing step. Burgbacher et al [79] extracted curve segments using k-NN [52] and compute the similarity between two segments by employing Dynamic Time Warping (DTW) [65]. To evaluate this approach, the authors performed a 10-fold cross-validation.…”

Section: ) Progressivementioning

confidence: 99%

“…The varying results can be attributed to different conditions during the time of recording (e.g., distance between the user and speaker for example; if a user is talking or the device is in the pocket), and background noise such as noise from air conditioner and keyboard typing. 9) Authentication based on Curve Training: Burgbacher et al [79] introduced a method that gathers information from users as they trace curves. First, the approach randomly generated 19 curves where the starting and ending points were shown to indicate the trace direction.…”

Section: ) Authenticating Using Keystroke Dynamics and Fingermentioning

confidence: 99%

Authentication of Smartphone Users Using Behavioral Biometrics

Alzubaidi

Kalita

2016

IEEE Commun. Surv. Tutorials

162

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Smartphones and tablets have become ubiquitous in our daily lives. Smartphones, in particular, have become more than personal assistants. These devices have provided new avenues for consumers to play, work and socialize whenever and wherever they want. Smartphones are small in size; so they are easy to handle and to stow and carry in users' pockets or purses. However, mobile devices are also susceptible to various problems. One of the greatest concerns is the possibility of breach in security and privacy if the device is seized by an outside party. It is possible that threats can come from friends as well as strangers. Due to the size of smart devices, they can be easily lost and may expose details of users' private lives. In addition, this might enable pervasive observation or imitation of one's movements and activities, such as sending messages to contacts, accessing private communication, shopping with a credit card, and relaying information about where one has been. This paper highlights the potential risks that occur when smartphones are stolen or seized, discusses the concept of continuous authentication, and analyzes current approaches and mechanisms of behavioral biometrics with respect to methodology, associated datasets and evaluation approaches.

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