Keystroke Inference Using Smartphone Kinematics

Oliver Buckley, Duncan Hodges, Melissa Hadgkiss, Sarah Morris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
15 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationInternational Conference on Human Aspects of Information Security, Privacy, and Trust
Subtitle of host publicationHAS 2017: Human Aspects of Information Security, Privacy and Trust
EditorsTheo Tryfonas
PublisherSpringer
Pages226-238
Number of pages13
Volume10292
ISBN (Electronic)978-3-319-58460-7
ISBN (Print)978-3-319-58459-1
DOIs
Publication statusPublished - 2017

Publication series

NameLecture Notes on Computer Science
PublisherSpringer
Volume10292
ISSN (Print)0302-9743

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