Cross‐device tracking through identification of user typing behaviours. Issue 15 (1st July 2018)

• Record Type: Journal Article
• Authors: Yuan, H.; Maple, C.; Chen, C.; Watson, T.
• Is Part Of: Electronics letters. Volume 54:Issue 15(2018)
• Page Start: 957
• Page End: 959
• Publication Date: 2018-07-01
• Subjects: Android (operating system) -- target tracking -- object tracking -- learning (artificial intelligence) -- image recognition; cross‐device tracking -- device user typing behaviour identification -- contact point angle -- contact point distance -- target character -- data collection -- Android App -- machine learning algorithms; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.0893
• Languages: English
• ISSNs: 0013-5194

Abstract

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.