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Authors: Gerardo Canfora 1 ; Giovanni Cappabianca 1 ; Pasquale Carangelo 1 ; Fabio Martinelli 2 ; Francesco Mercaldo 2 ; Ernesto Rosario Russo 1 and Corrado Aaron Visaggio 1

Affiliations: 1 University of Sannio, Italy ; 2 National Research Council of Italy (CNR), Italy

ISBN: 978-989-758-259-2

Keyword(s): Continuous Authentication, Silent Authentication, Security, Behavioral Models, Android.

Related Ontology Subjects/Areas/Topics: Information and Systems Security ; Security and Privacy in Mobile Systems ; Security in Information Systems ; Security Metrics and Measurement

Abstract: The last years have seen a growing explosion of the use of mobile devices. As matter of fact “smart” devices are used for a plethora of activities: from spending leisure time on social networks to e-banking. For these reasons smart devices hold huge volumes of private and sensitive user data and allow the access to critical applications in terms of privacy and security. Currently mobile devices provide an authentication mechanism based on the login: they do not continuously verify the identity of the user while sensitive activities are performed. This mechanism may allow an adversary to access sensitive information about users and to replace them during sensitive tasks, once they have obtained the user’s credentials. To mitigate this risk, in this paper we propose a method for the silent and continuous authentication. Considering that each user typically runs recurrently a certain set of applications in every-day life, our method extracts this characterizing sequences of apps for prof iling the user and recognizing the user of the device that is not the owner. Using machine learning techniques several classifiers have been trained and the effectiveness of the proposed method has been evaluated by modeling the user behavior of 15 volunteer participants. Encouraging results have been obtained, i.e. a precision in distinguishing an impostor from the owner equal to 99%. The main benefit of this method is that is does not use sensitive data, nor biometrics, which, if compromised, cannot be replaced. (More)


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Paper citation in several formats:
Canfora, G.; Cappabianca, G.; Carangelo, P.; Martinelli, F.; Mercaldo, F.; Russo, E. and Visaggio, C. (2017). Mobile Silent and Continuous Authentication using Apps Sequence.In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 4: SECRYPT, (ICETE 2017) ISBN 978-989-758-259-2, pages 79-91. DOI: 10.5220/0006424200790091

author={Gerardo Canfora. and Giovanni Cappabianca. and Pasquale Carangelo. and Fabio Martinelli. and Francesco Mercaldo. and Ernesto Rosario Russo. and Corrado Aaron Visaggio.},
title={Mobile Silent and Continuous Authentication using Apps Sequence},
booktitle={Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 4: SECRYPT, (ICETE 2017)},


JO - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 4: SECRYPT, (ICETE 2017)
TI - Mobile Silent and Continuous Authentication using Apps Sequence
SN - 978-989-758-259-2
AU - Canfora, G.
AU - Cappabianca, G.
AU - Carangelo, P.
AU - Martinelli, F.
AU - Mercaldo, F.
AU - Russo, E.
AU - Visaggio, C.
PY - 2017
SP - 79
EP - 91
DO - 10.5220/0006424200790091

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