Mobile user signature extraction based on user behavioural pattern (MUSEP)

Mobile user signature extraction based on user behavioural pattern (MUSEP) PurposeThe purpose of this paper is to extract the user behaviour and transform it into a unique signature that can be used as implicit authentication technique. Smart devices are equipped with multiple authentication techniques and still remain prone to attacks because all of these techniques require explicit intervention of the user. Entering a pin code, a password or even having a biometric print can be easily hacked by an adversary.Design/methodology/approachIn this paper, the authors introduce a novel authentication model to be used as complementary to the existing authentication models. Particularly, the duration of usage of each application and the occurrence time were examined and modelled into a user signature. During the learning phase, a cubic spline function is used to extract the user signature based on his/her behavioural pattern.FindingsPreliminary field experiments show a 70 per cent accuracy rate in determining the rightful owner of the device.Originality/valueThe main contribution of this paper is a framework that extracts the user behaviour and transforms it into a unique signature that can be used to implicitly authenticate the user. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png international Journal of Pervasive Computing and Communications Emerald Publishing

Mobile user signature extraction based on user behavioural pattern (MUSEP)

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1742-7371
DOI
10.1108/IJPCC-05-2016-0025
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to extract the user behaviour and transform it into a unique signature that can be used as implicit authentication technique. Smart devices are equipped with multiple authentication techniques and still remain prone to attacks because all of these techniques require explicit intervention of the user. Entering a pin code, a password or even having a biometric print can be easily hacked by an adversary.Design/methodology/approachIn this paper, the authors introduce a novel authentication model to be used as complementary to the existing authentication models. Particularly, the duration of usage of each application and the occurrence time were examined and modelled into a user signature. During the learning phase, a cubic spline function is used to extract the user signature based on his/her behavioural pattern.FindingsPreliminary field experiments show a 70 per cent accuracy rate in determining the rightful owner of the device.Originality/valueThe main contribution of this paper is a framework that extracts the user behaviour and transforms it into a unique signature that can be used to implicitly authenticate the user.

Journal

international Journal of Pervasive Computing and CommunicationsEmerald Publishing

Published: Nov 7, 2016

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