Supervised classification methods applied to keystroke dynamics through mobile devices

Mendizábal-Vázquez, Ignacio de; Santos-Sierra, Daniel de; Guerra-Casanova, Javier; Ávila, Carmen Sánchez

doi:10.1109/ccst.2014.6987033

Cited by 20 publications

(14 citation statements)

References 12 publications

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“…Techniques used in this operation include outlier detection and removal (Zheng et al, 2014). A dimension reduction technique may also be used to ensure that raw data remain small yet representable, for the sake of computational efficiency on resource limited mobile devices (de Mendizabal-Vazquez et al, 2014).…”

Section: Fig 3 a Touch Dynamics Authentication Frameworkmentioning

confidence: 99%

“…This is because each touch event generates more than one movement and rotation values. To make the data usable as feature data, we should apply some statistical computations, such as min, max, mean and variance, on the raw data, and the results of these computations can be used as meaningful feature data (de Mendizabal-Vazquez et al, 2014;Ho, 2013). Also, as (Zheng et al, 2014) pointed out, both sensors are sensitive to tiny movement changes.…”

Section: Motion Feature (Mo)mentioning

confidence: 99%

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A survey on touch dynamics authentication in mobile devices

Teh

Zhang

Teoh

et al. 2016

Computers & Security

125

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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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Section: Fig 3 a Touch Dynamics Authentication Frameworkmentioning

confidence: 99%

Section: Motion Feature (Mo)mentioning

confidence: 99%

A survey on touch dynamics authentication in mobile devices

Teh

Zhang

Teoh

et al. 2016

Computers & Security

125

View full text Add to dashboard Cite

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“…The work reported in (Vazquez et al 2014) was carried out to test the applicability of verifying subjects based on touch dynamics using numerical-based input strings. In their experiments, some of the touch dynamics features were extracted by using the more sophisticated accelerometer and gyroscope sensors.…”

Section: Related Workmentioning

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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“…Thus, KDA is a cheap biometric authentication [9], because it only uses existing devices. The advantage of KDA is that someone will not realize that the system used KDA [10].…”

Section: Keystroke Dynamic Authenticationmentioning

confidence: 99%

Keystroke Dynamic Authentication Using Combined MHR (Mean of Horner’s Rules) and Standard Deviation

Chandranegara

Sumadi

2018

KINETIK

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Keystroke Dynamic Authentication used a behavior to authenticate the user and one of biometric authentication. The behavior used a typing speed a character on the keyboard and every user had a unique behavior in typing. To improve classification between user and attacker of Keystroke Dynamic Authentication in this research, we proposed a combination of MHR (Mean of Horner’s Rules) and standard deviation. The results of this research showed that our proposed method gave a high accuracy (93.872%) than the previous method (75.388% and 75.156%). This research gave an opportunity to implemented in real login system because our method gave the best results with False Acceptance Rate (FAR) is 0.113. The user can be used as a simple password and ignore a worrying about an account hacking in the system.

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Supervised classification methods applied to keystroke dynamics through mobile devices

Cited by 20 publications

References 12 publications

A survey on touch dynamics authentication in mobile devices

A survey on touch dynamics authentication in mobile devices

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

Keystroke Dynamic Authentication Using Combined MHR (Mean of Horner’s Rules) and Standard Deviation

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