Keystroke dynamics-based authentication for mobile devices

Hwang, Seong-seob; Cho, Sungzoon; Park, Sunghoon

doi:10.1016/j.cose.2008.10.002

Cited by 96 publications

(72 citation statements)

References 8 publications

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“…Campisi et al [12] analyzed a typing scenario with alphabetic strings on numeric keyboards and obtained a 13.59% EER using a statistical classifier. Hwang et al [24] reported accuracy improvements for short PIN lengths when using artificial rhythms and tempo cues. This strategy decreased their EERs from 13% to 4%.…”

Section: Keystroke Dynamics On Hardware Keyboards For Mobile Devicesmentioning

confidence: 99%

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I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

Giuffrida¹,

Majdanik²,

Conti

et al. 2014

Lecture Notes in Computer Science

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Abstract. Mobile devices have become an important part of our everyday life, harvesting more and more confidential user information. Their portable nature and the great exposure to security attacks, however, call out for stronger authentication mechanisms than simple password-based identification. Biometric authentication techniques have shown potential in this context. Unfortunately, prior approaches are either excessively prone to forgery or have too low accuracy to foster widespread adoption.In this paper, we propose sensor-enhanced keystroke dynamics, a new biometric mechanism to authenticate users typing on mobile devices. The key idea is to characterize the typing behavior of the user via unique sensor features and rely on standard machine learning techniques to perform user authentication. To demonstrate the effectiveness of our approach, we implemented an Android prototype system termed Unagi. Our implementation supports several feature extraction and detection algorithms for evaluation and comparison purposes. Experimental results demonstrate that sensor-enhanced keystroke dynamics can improve the accuracy of recent gestured-based authentication mechanisms (i.e., EER > 0.5%) by one order of magnitude, and the accuracy of traditional keystroke dynamics (i.e., EER > 7%) by two orders of magnitude.

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Section: Keystroke Dynamics On Hardware Keyboards For Mobile Devicesmentioning

confidence: 99%

“…[4] 1.45% F RR, 1.89% F AR Allows 1 authentication failure. [26] 3.80% EER [28] 7.10% EER Hardware (Mobile device) [13] 10.40% EER [27] 12.20% EER [12] 13.59% EER [24] 4.00% EER Use of artificial rhythms. [60] 0.00% F RR, 2.00% F AR Allows 1 authentication failure.…”

Section: Keyboardmentioning

confidence: 99%

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

Giuffrida¹,

Majdanik²,

Conti

et al. 2014

Lecture Notes in Computer Science

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show abstract

“…The authors focus on biometrics on mobile phone through some standard modalities (fingerprint, speaker recognition, iris recognition, gait) and propose a new application to ECG measurement and remote telecardiology, with an extra portable heart monitoring device. Some recent papers [15], [16], and [17] deal with keystroke dynamics based recognition. The first paper makes a study about user identification using keystroke dynamics-based authentication (KDA) on mobile devices, relying on 11-digit telephone numbers and text messages as well as 4-digit PINs to classify users.…”

Section: Biometric Pattern Based Authenticationmentioning

confidence: 99%

Synchronous One Time Biometrics with Pattern Based Authentication

Lacharme

Rosenberger

2016

2016 11th International Conference on Availability, Reliability and Security (ARES)

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Abstract-One time passwords are commonly used for authentication purposes in electronic transactions. Nevertheless, providing such a one time password is not really a strong authentication proof because the token generating the passwords can be given by an impostor. In order to cope with this problem, biometric recognition is more and more employed. Even if biometric data are strongly linked with the user, their revocability nor diversity is possible, without an adapted postprocessing. Biometric template protection schemes, including the BioHashing algorithm, are used to manage the underlying privacy and security issues. These schemes are used for the protection of several biometric modalities, but are not necessary adapted for all of them. In this paper, we propose a new protocol combining protected biometric data and a classical synchronous one time password to enhance the security of user authentication while preserving usability and privacy. Behavioral biometrics is used to provide a fast and a usable solution for users. We show through experimental results the efficiency of the proposed method.

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“…Keystroke dynamics verification is based on how the user type in on a keyboard or other generic interfaces equipped with keys, which may belong to a PC, or mobile devices. The keystroke of a person has a unique pattern [1][2][3][4][5][6][7][8][9][10][11]. The relative order in which users press and release keys can vary greatly from user to user, especially while typing words or phrases in which each user has a more established typing pattern.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fortified Password Generator System Using Special Characters

Jeong

Kim

2015

Int. J. Fuzzy Log. Intell. Syst.

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The developed security scheme for user authentication, which uses both a password and the various devices, is always open by malicious user. In order to solve that problem, a keystroke dynamics is introduced. A person's keystroke has a unique pattern. That allows the use of keystroke dynamics to authenticate users. However, it has a problem to authenticate users because it has an accuracy problem. And many people use passwords, for which most of them use a simple word such as "password" or numbers such as "1234." Despite people already perceive that a simple password is not secure enough, they still use simple password because it is easy to use and to remember. And they have to use a secure password that includes special characters such as "#!(*)ˆ". In this paper, we propose the automatic fortified password generator system which uses special characters and keystroke feature. At first, the keystroke feature is measured while user key in the password. After that, the feature of user's keystroke is classified. We measure the longest or the shortest interval time as user's keystroke feature. As that result, it is possible to change a simple password to a secure one simply by adding a special character to it according to the classified feature. This system is effective even when the cyber attacker knows the password.

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Keystroke dynamics-based authentication for mobile devices

Cited by 96 publications

References 8 publications

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

Synchronous One Time Biometrics with Pattern Based Authentication

Automatic Fortified Password Generator System Using Special Characters

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