Performance Analysis of Touch-Interaction Behavior for Active Smartphone Authentication

Shen, Chao; Zhang, Yong; Guan, Xiaohong; Maxion, Roy A.

doi:10.1109/tifs.2015.2503258

Cited by 85 publications

(54 citation statements)

References 21 publications

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“…However, due to the simplicity and easy guessability of the PIN and pattern passcodes [ 12 ] (e.g., shoulder surfing attack [ 13 ] and smudge attack [ 8 ]), and the risk of fingerprint loss from public events (e.g., attackers can get fingerprints from public events with the help of a standard camera [ 9 ]), a growing number of biometric features ( i.e. , signature or gesture based methods [ 14 , 15 ], touch dynamics [ 16 , 17 ], and keystroke dynamics [ 18 , 19 ]) has been applied to strengthen smartphone authentication [ 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Background and Related Workmentioning

confidence: 99%

“…, pressure, area, duration, position, velocity, and acceleration), and employed a support vector machine (SVM) to perform user authentication task in a lab scenario. Shen et al [ 17 ] developed a continuous smartphone authentication system based on users’ touch-sliding operations. The system analyzed four types of touch behavior ( i.e.…”

Section: Background and Related Workmentioning

confidence: 99%

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Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones

Shen

Yuan

et al. 2016

Sensors

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The growing trend of using smartphones as personal computing platforms to access and store private information has stressed the demand for secure and usable authentication mechanisms. This paper investigates the feasibility and applicability of using motion-sensor behavior data for user authentication on smartphones. For each sample of the passcode, sensory data from motion sensors are analyzed to extract descriptive and intensive features for accurate and fine-grained characterization of users’ passcode-input actions. One-class learning methods are applied to the feature space for performing user authentication. Analyses are conducted using data from 48 participants with 129,621 passcode samples across various operational scenarios and different types of smartphones. Extensive experiments are included to examine the efficacy of the proposed approach, which achieves a false-rejection rate of 6.85% and a false-acceptance rate of 5.01%. Additional experiments on usability with respect to passcode length, sensitivity with respect to training sample size, scalability with respect to number of users, and flexibility with respect to screen size were provided to further explore the effectiveness and practicability. The results suggest that sensory data could provide useful authentication information, and this level of performance approaches sufficiency for two-factor authentication on smartphones. Our dataset is publicly available to facilitate future research.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones

Shen

Yuan

et al. 2016

Sensors

Self Cite

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“…This method can be implemented by employing existing sensors embedded in a mobile device, so it is comparatively cheaper than other biometrics authentication method. In addition, this method is non-intrusive and can operate in parallel with a person's normal mobile device usage activities (Shen et al, 2016). The existing passcode authentication method has a wide social acceptance, and the touch dynamics authentication method is also expected to be widely acceptable by the general public (Campisi et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A survey on touch dynamics authentication in mobile devices

Teh

Zhang

Teoh

et al. 2016

Computers & Security

130

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There have been research activities in the area of keystroke dynamics biometrics on physical keyboards (desktop computers or conventional mobile phones) undertaken in the past three decades. However, in terms of touch dynamics biometrics on virtual keyboards (modern touchscreen mobile devices), there has been little published work. Particularly, there is a lack of an extensive survey and evaluation of the methodologies adopted in the area. Owing to the widespread use of touchscreen mobile devices, it is necessary for us to examine the techniques and their effectiveness in the domain of touch dynamics biometrics. The aim of this paper is to provide some insights and comparative analysis of the current state-of-the-art in the topic area, including data acquisition protocols, feature data representations, decision making techniques, as well as experimental settings and evaluations. With such a survey, we can gain a better understanding of the current state-of-the-art, thus identifying challenging issues and knowledge gaps for further research.

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“…Leaving aside the signature (for a while), it has been already showed that the interaction with the touchscreen results in different patterns (e.g. swipe or touch dynamics) can be used for user identification aims [23], [88]. In general, high intra-class variability due to time, emotional state, change of the part of the screen which has been touched and task performed has been observed [23].…”

Section: A Accessibility and Usabilitymentioning

confidence: 99%

Automatic Signature Verification in the Mobile Cloud Scenario: Survey and Way Ahead

Impedovo

Pirlo

2021

IEEE Trans. Emerg. Topics Comput.

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On-line signature verification is typically carried out with the use of digitizing tablets specifically designed for the aim. So far, stand-alone systems have been mainly inspected, but the current distributed/cloud scenario and the amount of mobile devices in everyday life is calling for a new challenge. Within this scenario, signatures are acquired around the world with different kinds of devices and processed on multiple platforms in order to be verified. Through the paper, the different phases of the signature verification process in the new scenario are presented and the most valuable results are discussed considering the following aspects: accessibility and usability, interoperability, security and performance. Achievements as well as weakness are focused in order to highlight promising directions for further research and technology development.

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Performance Analysis of Touch-Interaction Behavior for Active Smartphone Authentication

Cited by 85 publications

References 21 publications

Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones

Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones

A survey on touch dynamics authentication in mobile devices

Automatic Signature Verification in the Mobile Cloud Scenario: Survey and Way Ahead

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