Keywords
Smart Devices
user authentication
How to Cite
Abstract
Nowadays, smart devices have become a part of our
lives, hold our data, and are used for sensitive transactions like
internet banking, mobile banking, etc. Therefore, it is crucial to
secure the data in these smart devices from theft or misplacement.
The majority of the devices are secured with password/PINbased
user authentication methods, which are already proved
a less secure or easily guessable user authentication method.
An alternative technique for securing smart devices is keystroke
dynamics. Keystroke dynamics (KSD) is behavioral biometrics,
which uses a natural typing pattern unique in every individual
and difficult to fake or replicates that pattern. This paper
proposes a user authentication model based on KSD as an additional
security method for increasing the smart devices’ security
level. In order to analyze the proposed model, an android-based
application has been implemented for collecting data from fake
and genuine users. Six machine learning algorithms have been
tested on the collected data set to study their suitability for use
in the keystroke dynamics-based authentication model.
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