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Cross‐device tracking through identification of user typing behaviours.

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Title: Cross‐device tracking through identification of user typing behaviours.
Authors: Yuan, H.1 (AUTHOR) H.Yuan.4@warwick.ac.uk, Maple, C.1 (AUTHOR), Chen, C.1 (AUTHOR), Watson, T.1 (AUTHOR)
Source: Electronics Letters (Wiley-Blackwell). Jul2018, Vol. 54 Issue 15, p957-959. 3p.
Abstract: A novel method of cross‐device tracking based on user typing behaviours is presented. Compared with existing methods, typing behaviours can offer greater security and efficiency. When people type on their devices, a number of different factors may be considered to identify users, such as the angle and distance of contact point to the centre of the target character, the time elapsed between two typing actions and the physical force exerted on the device (which can be measured by an accelerometer). An experiment was conducted to validate the proposed model; those data are collected through an Android App developed for the purpose of this study. By collecting a reasonable amount of this type of data, it is shown that machine learning algorithms can be employed to first classify different users and subsequently authenticate users across devices. [ABSTRACT FROM AUTHOR]
Copyright of Electronics Letters (Wiley-Blackwell) is the property of Wiley-Blackwell 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.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: Cross‐device tracking through identification of user typing behaviours.
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  Data: <searchLink fieldCode="AR" term="%22Yuan%2C+H%2E%22">Yuan, H.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> H.Yuan.4@warwick.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Maple%2C+C%2E%22">Maple, C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+C%2E%22">Chen, C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Watson%2C+T%2E%22">Watson, T.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Electronics+Letters+%28Wiley-Blackwell%29%22">Electronics Letters (Wiley-Blackwell)</searchLink>. Jul2018, Vol. 54 Issue 15, p957-959. 3p.
– Name: Abstract
  Label: Abstract
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  Data: A novel method of cross‐device tracking based on user typing behaviours is presented. Compared with existing methods, typing behaviours can offer greater security and efficiency. When people type on their devices, a number of different factors may be considered to identify users, such as the angle and distance of contact point to the centre of the target character, the time elapsed between two typing actions and the physical force exerted on the device (which can be measured by an accelerometer). An experiment was conducted to validate the proposed model; those data are collected through an Android App developed for the purpose of this study. By collecting a reasonable amount of this type of data, it is shown that machine learning algorithms can be employed to first classify different users and subsequently authenticate users across devices. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Electronics Letters (Wiley-Blackwell) is the property of Wiley-Blackwell 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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        Value: 10.1049/el.2018.0893
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        Text: English
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      – TitleFull: Cross‐device tracking through identification of user typing behaviours.
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            NameFull: Yuan, H.
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            NameFull: Maple, C.
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            NameFull: Chen, C.
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            – D: 01
              M: 07
              Text: Jul2018
              Type: published
              Y: 2018
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