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Improving static audio keystroke analysis by score fusion of acoustic and timing data.

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Title: Improving static audio keystroke analysis by score fusion of acoustic and timing data.
Authors: Pleva, Matúš1, Ondáš, Stanislav1, Juhár, Jozef1, Bours, Patrick2
Source: Multimedia Tools & Applications. Dec2017, Vol. 76 Issue 24, p25749-25766. 18p.
Subjects: Biometric identification, Keystroke timing authentication, Computer access control, Computer operating systems, Self-organizing maps, Hidden Markov models
Abstract: In this paper we investigate the capacity of sound & timing information during typing of a password for the user identification and authentication task. The novelty of this paper lies in the comparison of performance between improved timing-based and audio-based keystroke dynamics analysis and the fusion for the keystroke authentication. We collected data of 50 people typing the same given password 100 times, divided into 4 sessions of 25 typings and tested how well the system could recognize the correct typist. Using fusion of timing (9.73%) and audio calibration scores (8.99%) described in the paper we achieved 4.65% EER (Equal Error Rate) for the authentication task. The results show the potential of using Audio Keystroke Dynamics information as a way to authenticate or identify users during log-on. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Pleva%2C+Matúš%22">Pleva, Matúš</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ondáš%2C+Stanislav%22">Ondáš, Stanislav</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Juhár%2C+Jozef%22">Juhár, Jozef</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Bours%2C+Patrick%22">Bours, Patrick</searchLink><relatesTo>2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Dec2017, Vol. 76 Issue 24, p25749-25766. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Biometric+identification%22">Biometric identification</searchLink><br /><searchLink fieldCode="DE" term="%22Keystroke+timing+authentication%22">Keystroke timing authentication</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+access+control%22">Computer access control</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+operating+systems%22">Computer operating systems</searchLink><br /><searchLink fieldCode="DE" term="%22Self-organizing+maps%22">Self-organizing maps</searchLink><br /><searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink>
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  Label: Abstract
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  Data: In this paper we investigate the capacity of sound & timing information during typing of a password for the user identification and authentication task. The novelty of this paper lies in the comparison of performance between improved timing-based and audio-based keystroke dynamics analysis and the fusion for the keystroke authentication. We collected data of 50 people typing the same given password 100 times, divided into 4 sessions of 25 typings and tested how well the system could recognize the correct typist. Using fusion of timing (9.73%) and audio calibration scores (8.99%) described in the paper we achieved 4.65% EER (Equal Error Rate) for the authentication task. The results show the potential of using Audio Keystroke Dynamics information as a way to authenticate or identify users during log-on. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Dec2017
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