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Keystroke Inference Using Smartphone Kinematics

  • Oliver Buckley
  • Duncan Hodges
  • Melissa Hadgkiss
  • Sarah Morris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10292)

Abstract

The use of smartphones is becoming ubiquitous in modern society, these very personal devices store large amounts of personal information and we use these devices to access everything from our bank to our social networks, we communicate using these devices in both open one-to-many communications and in more closed, private one-to-one communications. In this paper we have created a method to infer what is typed on a device purely from how the device moves in the user’s hand. With very small amounts of training data (less than the size of a tweet) we are able to predict the text typed on a device with accuracies of up to 90%. We found no effect on this accuracy from how fast users type, how comfortable they are using smartphone keyboards or how the device was held in the hand. It is trivial to create an application that can access the motion data of a phone whilst a user is engaged in other applications, the accessing of motion data does not require any permission to be granted by the user and hence represents a tangible threat to smartphone users.

Keywords

Motion Sensor Rotation Vector Acceleration Vector Acceleration Sensor Dynamic Text 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Oliver Buckley
    • 1
  • Duncan Hodges
    • 1
  • Melissa Hadgkiss
    • 2
  • Sarah Morris
    • 2
  1. 1.Centre for Electronic Warfare, Information and CyberCranfield University, Defence Academy of the United KingdomSwindonUK
  2. 2.Cranfield Forensic InstituteCranfield University, Defence Academy of the United KingdomSwindonUK

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