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Mobile based continuous authentication using deep features

Lookup NU author(s): Mario Parreño Centeno, Dr Yu GuanORCiD, Professor Aad van Moorsel


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© 2018 Association for Computing Machinery. Continuous authentication is a promising approach to validate the user’s identity during a work session, e.g., for mobile banking applications. Recently, it has been demonstrated that changes in the motion patterns of the user may help to note the unauthorised use of mobile devices. Several approaches have been proposed in this area but with relatively weak performance results. In this work, we propose an approach which uses a Siamese convolutional neural network to learn the signatures of the motion patterns from users and achieve a competitive verification accuracy up to 97.8%. We also find our algorithm is not very sensitive to sampling frequency and the length of the sequence.

Publication metadata

Author(s): Centeno MP, Guan Y, van Moorsel A

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: EMDL 2018 - Proceedings of the 2018 International Workshop on Embedded and Mobile Deep Learning

Year of Conference: 2018

Pages: 19-24

Online publication date: 15/06/2018

Acceptance date: 02/04/2018

Publisher: Association for Computing Machinery, Inc


DOI: 10.1145/3212725.3212732

Library holdings: Search Newcastle University Library for this item

ISBN: 9781450358446