Abstract
In this chapter we evaluate mobile active authentication based on an ensemble of biometrics and behavior-based profiling signals. We consider seven different data channels and their combination. Touch dynamics (touch gestures and keystroking), accelerometer, gyroscope, WiFi, GPS location and app usage are all collected during human-mobile interaction to authenticate the users. We evaluate two approaches: one-time authentication and active authentication. In one-time authentication, we employ the information of all channels available during one session. For active authentication we take advantage of mobile user behavior across multiple sessions by updating a confidence value of the authentication score. Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. This database comprises of smartphone sensor signals acquired during natural human-mobile interaction. Our results show that different traits can be complementary in terms of mobile user authentication and multimodal systems clearly increase the performance when compared to individual biometrics systems with accuracies ranging from 82.2% to 98.0% depending on the authentication scenario.
Keywords
The present chapter is adapted from the conference paper A. Acien et al. “MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns”, in ACM Intl. Conf. on Multimedia, Workshop on Multimodal Understanding and Learning for Embodied Applications (MULEA), pp. 53–59, Nice, France, October 2019. The new material here includes Table I and Sect. 5.3.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Indicate the value below which a given percentage of observation (samples in this case) in a group of observation falls.
- 2.
EER refers to the value where False Acceptance Rate (percentage of impostors classified as genuine) and False Rejection Rate (percentage of genuine users classified as impostors) are equal.
References
Radicati S (2018) Mobile statistics report, 2014–2018. The Radicati Group, INC. A Techonology Market Research Firm, Palo Alto
Cho G, Huh JH, Cho J, Oh S, Song Y, Kim H (2017) SysPal: system-guided pattern locks for android. In: Proceedings of IEEE Symposium on Security and Privacy, California, UE
Harbach M, von Zezschwitz E, Fichtner A, Luca AD, Smith M (2014) It’s a hard lock life: a field study of smartphone (un)locking behavior and risk perception. In: Proceedings of symposium on usable privacy and security, California, USA
Molla R (2018) Mary Meeker’s 2018 internet trends report: all the slides, plus analysis. In Recode
Martinez-Diaz M, Fierrez J, Galbally J (2016) Graphical password-based user authentication with free-form doodles. IEEE Trans Human-Machine Syst 46(4):607–661
Crouse D, Han H, Chandra D, Barbello B, Jain AK (2015) Continuous authentication of Mobile user: fusion of face image and inertial measurement unit data. In: Proceedings of IAPR international conference on biometrics, Phuket, Thailand
Patel VM, Chellappa R, Chandra D, Barbello B (2016) Continuous user authentication on mobile devices: recent progress and remaining challenges. IEEE Signal Process Mag 33(4):49–61
Mahbub U, Sarkar S, Patel VM, Chellappa R (2016) Active user authentication for smartphones: a challenge data set and benchmark results. In: Proceedings of IEEE 8th international conference on biometrics theory, applications and systems, New York, USA
Fierrez J, Pozo A, Martinez-Diaz M, Galbally J, Morales A (2018) Benchmarking touchscreen biometrics for Mobile authentication. IEEE Trans Inf Forensics Sec 13(11):2720–2733
G. Li and P. Bours (2018). Studying WiFi and accelerometer data based authentication method on mobile phones. In: Proceedings of 2nd international conference on biometric engineering and applications, Amsterdam, Netherlands
Buschek D, De Luca A, Alt F (2015) Improving accuracy, applicability and usability of keystroke biometrics on mobile touchscreen devices. In: Proceedings of 33rd annual ACM conference on human factors in computing systems, Seoul, Republic of Korea
Li G, Bours P (2018) A novel mobilephone application authentication approach based on accelerometer and gyroscope data. In: Proceedings of 17th international conference of the biometrics specials interest group, Fraunhofer, Germany
Mahbub U, Chellappa R (2016) PATH: person authentication using trace histories. In: Proceedings of ubiquitous computing, electronics & mobile communication conference, IEEE, New York, USA
Mahbub U, Komulainen J, Ferreira D, Chellappa R (2018) Continuous authentication of smartphones based on application usage. IEEE Transactions on Biometrics, Behavior, and Identity Science 1(3):165–180
Monaco JV, Tappert CC (2018) The partially observable hidden Markov model and its application to keystroke dynamics. Pattern Recogn 76:449–462
Fierrez J, Morales A, Vera-Rodriguez R, Camacho D (2018) Multiple classifiers in biometrics. Part 2: trends and challenges. Inf Fusion 44:103–112
Marcel S, Nixon MS, Fierrez J, Evans N (2019) Handbook of biometric anti-spoofing. presentation attack detection, Advances in computer vision and pattern recognition. Springer, Cham
Shi W, Yang J, Jiang Y, Yang F, Xiong Y (2011) Senguard: passive user identification on smartphones using multiple sensors. In: Proceedings of 7th IEEE international conference on wireless and mobile computing, networking and communications, Shangai, China, pp 141–148
Fridman L, Weber S, Greenstadt R, Kam M (2015) Active authentication on mobile devices via stylometry, GPS location, web browsing behavior, and application usage patterns. IEEE Syst J 11(2):513–521
Liu X, Shen C, Chen Y (2018) Multi-source interactive behavior analysis for continuous user authentication on smartphones. In: Proceedings of Chinese conference on biometric recognition, Urumchi, China
Li G, Bours P (2018) A mobile app authentication approach by fusing the scores from multi-modal data. In: Proceedings of 21st international conference on information fusion, Cambridge, UK
Deb D, Ross A, Jain AK, Prakah-Asante K,Prasad KV (2019) Actions Speak Louder Than (Pass) words: Passive Authentication of Smartphone Users via Deep Temporal Features. In: Proceedings of the 12th IAPR International Conference on Biometrics, Crete, Greece
Martinez-Diaz M, Fierrez J, Krish RP, Galbally J (2014) Mobile signature verification: feature robustness and performance comparison. IET Biometrics 3(4):267–277
O’Neal M, Balagani K, Phoha V, Rosenberg A, Serwadda A, Karim ME (2016) Context-aware active authentication using touch gestures, typing patterns and body movement. Louisiana Tech University, Ruston
Morales JF, Tolosana R, Ortega-Garcia J, Galbally J, Gomez-Barrero M, Anjos A (2016) Keystroke biometrics ongoing competition. IEEE Access 4:7736–7746
Perera P, Patel VM (2018) Efficient and low latency detection of intruders in mobile active authentication. IEEE Trans Inf Forensics Secur 13(6):1392–1405
Ernst R (2019) Mobile phone afterlife – why the second-hand market will be all the rage in 2019. In RCR Wireless News
Acknowledgments
This work was funded by the projects BIBECA (RTI2018-101248-B-I00 MINECO/FEDER) and Bio-Guard (Ayudas Fundación BBVA a Equipos de Investigación Científica 2017), and by CECABANK.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Acien, A., Morales, A., Vera-Rodriguez, R., Fierrez, J. (2020). Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns. In: Bourlai, T., Karampelas, P., Patel, V.M. (eds) Securing Social Identity in Mobile Platforms. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-39489-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-39489-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39488-2
Online ISBN: 978-3-030-39489-9
eBook Packages: Computer ScienceComputer Science (R0)