Touch me once and i know it's you!

Luca, Alexander De; Hang, Alina; Brudy, Frederik; Lindner, Christian; Hußmann, Heinrich

doi:10.1145/2207676.2208544

Cited by 340 publications

(51 citation statements)

References 28 publications

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“…For example, recall-based drawing schemes can be extended to not only authenticate a user based on the drawn shape but also on how it has been drawn, based on finger pressure [34,17] and the effective finger size [17] on a capacitive touchscreen. Other sensors, such as accelerometer, gyroscope, location, and camera, could also potentially enhance authentication.…”

Section: Interaction Methodsmentioning

confidence: 99%

Exploring the design space of graphical passwords on smartphones

Schaub

Walch

Könings

et al. 2013

Proceedings of the Ninth Symposium on Usable Privacy and Security

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Smartphones have emerged as a likely application area for graphical passwords, because they are easier to input on touchscreens than text passwords. Extensive research on graphical passwords and the capabilities of modern smartphones result in a complex design space for graphical password schemes on smartphones. We analyze and describe this design space in detail. In the process, we identify and highlight interrelations between usability and security characteristics, available design features, and smartphone capabilities. We further show the expressiveness and utility of the design space in the development of graphical passwords schemes by implementing five different existing graphical password schemes on one smartphone platform. We performed usability and shoulder surfing experiments with the implemented schemes to validate identified relations in the design space. From our results, we derive a number of helpful insights and guidelines for the design of graphical passwords.

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Section: Interaction Methodsmentioning

confidence: 99%

Exploring the design space of graphical passwords on smartphones

Schaub

Walch

Könings

et al. 2013

Proceedings of the Ninth Symposium on Usable Privacy and Security

View full text Add to dashboard Cite

show abstract

“…To overcome this limitation, some authors (El-Abed et al 2014; Tasia et al 2014) suggest to break a single data acquisition session into multiple sub-sessions that are separated by some intervals, and then to combine the data acquired in the sub-sessions into a single set. However, this may means that the subject participation rate will be lower (De Luca et al 2012). As a result, the sample size of the dataset may be reduced.…”

Section: Defining a Data Acquisition Proceduresmentioning

confidence: 99%

Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

Teh

Zhang

Tan

et al. 2019

J Ambient Intell Human Comput

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Mobile devices, particularly the touch screen mobile devices, are increasingly used to store and access private and sensitive data or services, and this has led to an increased demand for more secure and usable security services, one of which is user authentication. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledgebased method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To overcome these limitations and to make the authentication service more secure and reliable for touch screen mobile device users, we have investigated the use of touch dynamics biometrics as a mobile device authentication solution by designing, implementing and evaluating a touch dynamics authentication method. This paper describes the design, implementation, and evaluation of this method, the acquisition of raw touch dynamics data, the use of the raw data to obtain touch dynamics features, and the training of the features to build an authentication model for user identity verification. The evaluation results show that by integrating the touch dynamics authentication method into the PIN-based authentication method, the protection levels against impersonation attacks is greatly enhanced. For example, if a PIN is compromised, the success rate of an impersonation attempt is drastically reduced from 100% (if only a 4-digit PIN is used) to 9.9% (if both the PIN and the touch dynamics are used).

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“…T. Feng, et al [21] proposed to extract finger motion speed and acceleration of touch patterns as features. Luca, et al [15] suggested to directly compute the distance between pattern traces using the dynamic time warping algorithm. Sae-Bae, et al [49] present 22 special touch patterns for user identification, most of which involve all five fingers simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Extended sammon projection and wavelet kernel extreme learning machine for gait-based legitimate user identification

Khan

Mazzara

Distefano

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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Smartphones have ubiquitously integrated into our home and work environments, however, the user normally relies on explicit but inefficient identification processes in a controlled environment. Therefore, when the device is stolen, the attacker can have access to the user's personal information and services against the stored password/s. As a result of this potential scenario, this work demonstrates the possibilities of legitimate user identification in a semicontrolled environment through the built-in smartphones motion dynamics captured by two different sensors. This is a two-fold process, sub-activity recognition based user identification using the wavelet kernel extreme learning machine (KELM) to first recognize the performed activity and then identifies whether the recognized activity is performed by the legitimate user or impostor. Prior to the identification; Extended Sammon Projection (ESP) method has been deployed to reduce the redundancy between the features. To validate the proposed system, we first collected data from 20 users walking with their device freely placed in one of their pants pockets (front right, front left, back right and back left pocket). Through extensive experimentation's using overall and averageone-subject-cross-validation, we demonstrate that together time and frequency domain features optimized by ESP to train the KELM is an effective system to identify the legitimate user or an impostor with 97 − 98% overall accuracy.

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Touch me once and i know it's you!

Cited by 340 publications

References 28 publications

Exploring the design space of graphical passwords on smartphones

Exploring the design space of graphical passwords on smartphones

Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

Extended sammon projection and wavelet kernel extreme learning machine for gait-based legitimate user identification

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