access icon openaccess Cross-device tracking through identification of user typing behaviours

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.

Inspec keywords: object tracking; learning (artificial intelligence); target tracking; image recognition; Android (operating system)

Other keywords: target character; Android App; contact point angle; cross-device tracking; contact point distance; device user typing behaviour identification; machine learning algorithms; data collection

Subjects: Computer vision and image processing techniques; Operating systems; Mobile, ubiquitous and pervasive computing; Image recognition; Knowledge engineering techniques

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http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.0893
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