University of Oulu

U. Mahbub, J. Komulainen, D. Ferreira and R. Chellappa, "Continuous Authentication of Smartphones Based on Application Usage," in IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 1, no. 3, pp. 165-180, July 2019. doi: 10.1109/TBIOM.2019.2918307

Continuous authentication of smartphones based on application usage

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Author: Mahbub, Upal1,2,3; Komulainen, Jukka4,5; Ferreira, Denzil4;
Organizations: 1Department of Electrical and Computer Engineering, University of Maryland at College Park, College Park, MD 20742 USA
2Center for Automation Research, UMIACS, University of Maryland at College Park, College Park, MD 20742, USA
3Qualcomm Technologies, Inc., San Diego, CA, USA
4Faculty of Information Technology and Electrical Engineering, University of Oulu, 90014 Oulu, Finland
5Visidon Ltd., Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.3 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2019-08-20


An empirical investigation of active/continuous authentication for smartphones is presented by exploiting users’ unique application usage data, i.e., distinct patterns of use, modeled by a Markovian process. Specifically, variations of hidden Markov models (HMMs) are evaluated for continuous user verification, and challenges due to the sparsity of session-wise data, an explosion of states, and handling unforeseen events in the test data are tackled. Unlike traditional approaches, the proposed formulation utilizes the complete app-usage information to achieve low latency. Through experimentation, empirical assessment of the impact of unforeseen events, i.e., unknown applications and unforeseen observations, on user verification is done via a modified edit-distance algorithm for sequence matching. It is found that for enhanced verification performance, unforeseen events should be considered. For validation, extensive experiments on two distinct datasets, namely, UMDAA-02 and Securacy, are performed. Using the marginally smoothed HMM a low equal error rate (EER) of 16.16% is reached for the Securacy dataset and the same method is found to be able to detect an intrusion within ~2.5 min of application use.

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Series: IEEE transactions on biometrics, behavior, and identity science
ISSN: 2637-6407
ISSN-E: 2637-6407
ISSN-L: 2637-6407
Volume: 1
Issue: 3
Pages: 165 - 180
DOI: 10.1109/TBIOM.2019.2918307
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported in part by the Emil Aaltonen Foundation under Grant 170117 KO, in part by the Finnish Foundation for Technology Promotion (PoDoCo Program), in part by the Academy of Finland (SENSATE) under Grant 286386-CPDSS, Grant 285459-iSCIENCE, Grant 304925-CARE, and Grant 313224-STOP, and in part by the Marie Skłodowska-Curie Actions under Grant 645706-GRAGE.
EU Grant Number: (645706) GRAGE - Grey and green in Europe: elderly living in urban areas
Academy of Finland Grant Number: 286386
Detailed Information: 286386 (Academy of Finland Funding decision)
285459 (Academy of Finland Funding decision)
304925 (Academy of Finland Funding decision)
313224 (Academy of Finland Funding decision)
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