Keystroke Inference Using Smartphone Kinematics

Buckley, Oliver ORCID: https://orcid.org/0000-0003-1502-5721, Hodges, Duncan, Hadgkiss, Melissa and Morris, Sarah (2017) Keystroke Inference Using Smartphone Kinematics. In: International Conference on Human Aspects of Information Security, Privacy, and Trust. Lecture Notes on Computer Science, 10292 . Springer, pp. 226-238. ISBN 978-3-319-58459-1

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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.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory
Depositing User:	Pure Connector
Date Deposited:	31 Jan 2018 13:30
Last Modified:	14 Mar 2023 08:37
URI:	https://ueaeprints.uea.ac.uk/id/eprint/66164
DOI:	10.1007/978-3-319-58460-7_15

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