Mobile authentication using keystroke dynamics

Dhage, Sudhir; Kundra, Pranav; Kanchan, Anish; Kap, Pratiksha

doi:10.1109/iccict.2015.7045746

Cited by 13 publications

(20 citation statements)

References 18 publications

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“…The third approach is to recruit a separate pool of subjects specifically for providing the illegitimate samples. Take (Dhage et al, 2015;Gascon et al, 2014;Sen and Muralidharan, 2014) for example, instead of requesting the subjects recruited for providing the legitimate samples, the research team recruited additional subjects for providing the illegitimate samples. In this way, it is more likely to obtain a balanced number of the legitimate and the illegitimate samples.…”

Section: Legitimate and Illegitimate Subject Samplesmentioning

confidence: 99%

“…Statistical (ST): There are several statistical techniques that have been used in biometrics research. These techniques include the mean and standard deviation (Tasia et al, 2014;Zheng et al, 2014) and the deviation tolerance (Dhage et al, 2015;Huang et al, 2012). There are a number of advantages associated with these techniques.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…The two scores are then combined into a single score for decision making. Methods such as the sum, weighted-sum, or product rules are commonly used to combine multiple scores (Dhage et al, 2015). If the scores from different machine learning techniques are not comparable, they will need to be normalized prior to fusion (Tresadern et al, 2013).…”

Section: Fusionmentioning

confidence: 99%

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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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Section: Legitimate and Illegitimate Subject Samplesmentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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A survey on touch dynamics authentication in mobile devices

Teh

Zhang

Teoh

et al. 2016

Computers & Security

130

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“…A similar approach proposed by Dhage et al used dwell time, fly time, and drift as features. A dataset was collected by asking 15 subjects to type 10 samples of a password consisting of 10 characters using a Sony Xperia M mobile device.…”

Section: Background and Related Workmentioning

confidence: 99%

Multimodal mobile keystroke dynamics biometrics combining fixed and variable passwords

Alshanketi

Traoré

Awad

2019

Security and Privacy

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Recent works have demonstrated the possibility to craft successful statistical attacks against keystroke dynamic biometric password. Those attacks leverage the possibility to capture several keystroke dynamics samples for a given password string, and then extract and use their distributional properties to craft the attack. These approaches are by design more likely to be successful when launched against fixed passwords, as several samples of the passwords can be captured through successive login sessions. Although the dynamics obtained from specific keys or key sequences for consecutive passwords slightly vary, by definition the distributional properties remain fairly stable for the same user. One way to thwart such that attack to use a variable password, also know as one‐time password (OTP). However, the fact that the keystroke dynamic OTP is different from one session to the other, makes it extremely difficult to reconstruct a valid biometric profile for a user. Modeling accurate keystroke dynamic OTP is challenging, due to the underlying variability and the sparse amount of information involved. We tackle the aformentioned challenge by presenting, in this paper, by presenting a multimodal approach tat combines fixed and variable keystroke dynamic biometric passwords. We investigate two different fusion models and evaluate our approach using a data set involving 100 different users, yielding encouraging performance results in terms of accuracy and resistance against statistical attacks.

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“…Experiments using characters as well as PIN have been conducted. Starting with EER 6.9% of the study using 6 characters [9], experiment using 6 to 8 characters showed EER of 21.02% [15], using 10 characters showed EER of 0.806% [16], and using 34 characters showed EER of 9.3% [17], respectively. Later, there was a new type of study that extracts pressure and time interval from image instead of keys [6].…”

Section: Related Workmentioning

confidence: 99%

Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors

Lee

Hwang

Kim

et al. 2018

Security and Communication Networks

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Personal Identification Numbers (PINs) and pattern drawing have been used as common authentication methods especially on smartphones. Such methods, however, are very vulnerable to the shoulder surfing attack. Thus, keystroke dynamics that authenticate legitimate users based on their typing manner have been studied for years. However, many of the studies have focused on PC keyboard keystrokes. More studies on mobile and smartphones keystroke dynamics are warranted; as smartphones make progress in both hardware and software, features from smartphones have been diversified. In this paper, using various features including keystroke data such as time interval and motion data such as accelerometers and rotation values, we evaluate features with motion data and without motion data. We also compare 5 formulas for motion data, respectively. We also demonstrate that opposite gender match between a legitimate user and impostors has influence on authenticating by our experiment results.

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Mobile authentication using keystroke dynamics

Cited by 13 publications

References 18 publications

A survey on touch dynamics authentication in mobile devices

A survey on touch dynamics authentication in mobile devices

Multimodal mobile keystroke dynamics biometrics combining fixed and variable passwords

Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors

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