DRAW-A-PIN: Authentication using finger-drawn PIN on touch devices

Nguyen, Toan Van; Sae-Bae, Napa; Memon, Nasir

doi:10.1016/j.cose.2017.01.008

Cited by 65 publications

(28 citation statements)

References 39 publications

(64 reference statements)

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“…Shahzad et al [16] proposed an authentication scheme based on users' particular behavior when they perform a touch gesture and a signature. Nguyen et al [13] proposed an authentication scheme called DRAW-A-PIN, which required users to draw a PIN on touchscreen instead of typing. Their system particularly employed a Content Analyzer and a Drawing Behaviour Analyzer to identify imposters.…”

Section: Related Workmentioning

confidence: 99%

SocialAuth: Designing Touch Behavioral Smartphone User Authentication Based on Social Networking Applications

Meng

Jiang³

et al. 2019

ICT Systems Security and Privacy Protection

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Modern smartphones expressed an exponential growth and have become a personal assistant in people's daily lives, i.e., keeping connected with peers. Users are willing to store their personal data even sensitive information on the phones, making these devices an attractive target for cyber-criminals. Due to the limitations of traditional authentication methods like Personal Identification Number (PIN), research has been moved to the design of touch behavioral authentication on smartphones. However, how to design a robust behavioral authentication in a long-term period remains a challenge due to behavioral inconsistency. In this work, we advocate that touch gestures could become more consistent when users interact with specific applications. In this work, we focus on social networking applications and design a touch behavioral authentication scheme called SocialAuth. In the evaluation, we conduct a user study with 50 participants and demonstrate that touch behavioral deviation under our scheme could be significantly decreased and kept relatively stable even after a long-term period, i.e., a single SVM classifier could achieve an average error rate of about 3.1% and 3.7% before and after two weeks, respectively.

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Section: Related Workmentioning

confidence: 99%

SocialAuth: Designing Touch Behavioral Smartphone User Authentication Based on Social Networking Applications

Meng

Jiang³

et al. 2019

ICT Systems Security and Privacy Protection

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“…Then, in Block6, users had to repeat once the same task considered in Block5 but this time in an invisible mode, i.e., we did not provide to the users any visualization of the numbers while drawing them. The main motivation of this novel acquisition scenario is to protect us against shoulder surfing attacks, as commented in [22]. In Block 7, users had to draw each number of the password inside each of the four available boxes (two times), considering first a visible mode (see Fig.…”

Section: B Mobiletouchdbmentioning

confidence: 99%

BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks

Tolosana

Vera-Rodríguez

Fiérrez

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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Passwords are still used on a daily basis for all kind of applications. However, they are not secure enough by themselves in many cases. This work enhances password scenarios through two-factor authentication asking the users to draw each character of the password instead of typing them as usual. The main contributions of this study are as follows: i) We present the novel MobileTouchDB public database, acquired in an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. This database contains more than 64K on-line character samples performed by 217 users, with 94 different smartphone models, and up to 6 acquisition sessions. ii) We perform a complete analysis of the proposed approach considering both traditional authentication systems such as Dynamic Time Warping (DTW) and novel approaches based on Recurrent Neural Networks (RNNs). In addition, we present a novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs) 1 . This approach combines the potential of DTW and RNNs to train more robust systems against attacks.A complete analysis of the proposed approach is carried out using both MobileTouchDB 2 and e-BioDigitDB 3 databases. Our proposed TA-RNN system outperforms the state of the art, achieving a final 2.38% Equal Error Rate, using just a 4digit password and one training sample per character. These results encourage the deployment of our proposed approach in comparison with traditional typed-based password systems where the attack would have 100% success rate under the same impostor scenario.

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“…Signing with a finger over a display also allows the taking into account of tapping behavior [276]. In fact, it has been shown that touch analysis can be used to recognize users [87,179].…”

Section: Signing On Touchscreensmentioning

confidence: 99%

A Perspective Analysis of Handwritten Signature Technology

Diaz¹,

et al. 2019

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Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject. CCS Concepts: • General and reference → Surveys and overviews; • Security and privacy → Biometrics; Access control; • Theory of computation → Pattern matching; • Applied computing → Document analysis; System forensics;

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DRAW-A-PIN: Authentication using finger-drawn PIN on touch devices

Cited by 65 publications

References 39 publications

SocialAuth: Designing Touch Behavioral Smartphone User Authentication Based on Social Networking Applications

SocialAuth: Designing Touch Behavioral Smartphone User Authentication Based on Social Networking Applications

BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks

A Perspective Analysis of Handwritten Signature Technology

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