[en] Smartphones became a person's constant companion. As the strictly personal devices they are, they gradually enable the replacement of well established activities as for instance payments, two factor authentication or personal assistants. In addition, Internet of Things (IoT) gadgets extend the capabilities of the latter even further. Devices such as body worn fitness trackers allow users to keep track of daily activities by periodically synchronizing data with the smartphone and ultimately with the vendor's computational centers in the cloud. These fitness trackers are equipped with an array of sensors to measure the movements of the device, to derive information as step counts or make assessments about sleep quality. We capture the raw sensor data from wrist-worn activity trackers to model a biometric behavior profile of the carrier. We establish and present techniques to determine rather the original person, who trained the model, is currently wearing the bracelet or another individual. Our contribution is based on CANDECOMP/PARAFAC (CP) tensor decomposition so that computational complexity facilitates: the execution on light computational devices on low precision settings, or the migration to stronger CPUs or to the cloud, for high to very high granularity. This precision parameter allows the security layer to be adaptable, in order to be compliant with the requirements set by the use cases. We show that our approach identifies users with high confidence.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN)
Disciplines :
Computer science
Author, co-author :
FALK, Eric ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
CHARLIER, Jérémy Henri J. ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
STATE, Radu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
no
Language :
English
Title :
Your Moves, Your Device: Establishing Behavior Profiles Using Tensors
Publication date :
November 2017
Event name :
Advanced Data Mining and Applications - 13th International Conference, ADMA 2017
Event date :
from 05-11-2017 to 06-11-2017
Audience :
International
Main work title :
Advanced Data Mining and Applications - 13th International Conference, ADMA 2017
Al-Bajjari, A.L., Yuan, L.: Optimized authentication scheme for web application. In: 2016 IEEE 9th International Conference on Service-Oriented Computing and Applications (SOCA), pp. 52–58, November 2016
Altmann, S.: Rotations, Quaternions, and Double Groups. Dover Books on Mathematics. Dover Publications, New York (2005)
Aviv, A.J., Gibson, K., Mossop, E., Blaze, M., Smith, J.M.: Smudge attacks on smartphone touch screens. In: Proceedings of the 4th USENIX Conference on Offensive Technologies, WOOT 2010, pp. 1–7. USENIX Association, Berkeley (2010)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
Bonneau, J., Herley, C., van Oorschot, P.C., Stajano, F.: The quest to replace passwords: a framework for comparative evaluation of web authentication schemes. In: 2012 IEEE Symposium on Security and Privacy, pp. 553–567, May 2012
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM, New York (1992)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Burns, A., Greene, B.R., McGrath, M.J., O’Shea, T.J., Kuris, B., Ayer, S.M., Stroiescu, F., Cionca, V.: Shimmer; a wireless sensor platform for noninvasive biomedical research. IEEE Sens. J. 10(9), 1527–1534 (2010)
Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition. Psychometrika 35(3), 283–319 (1970)
Feng, T., Liu, Z., Kwon, K.A., Shi, W., Carbunar, B., Jiang, Y., Nguyen, N.: Continuous mobile authentication using touchscreen gestures. In: 2012 IEEE Conference on Technologies for Homeland Security, pp. 451–456, November 2012
Harshman, R.A.: Foundations of the PARAFAC procedure: models and conditions for an “explanatory” multi-modal factor analysis (1970)
Hu, J.S., Sun, K.C.: A robust orientation estimation algorithm using MARG sensors. IEEE Trans. Instrum. Meas. 64(3), 815–822 (2015)
Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybernet. SMC 15(4), 580–585 (1985)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Mahfoodh, A.T., Radha, H.: Compression of image ensembles using tensor decomposition. In: Picture Coding Symposium, pp. 21–24. IEEE (2013)
Nickel, C., Wirtl, T., Busch, C.: Authentication of smartphone users based on the way they walk using k-nn algorithm. In: 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 16–20, July 2012
Papalexakis, E.E., Pelechrinis, K., Faloutsos, C.: Location based social network analysis using tensors and signal processing tools. In: 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 93–96. IEEE (2015)
Parate, A., Chiu, M.C., Chadowitz, C., Ganesan, D., Kalogerakis, E.: RISQ: recognizing smoking gestures with inertial sensors on a wristband. In: Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2014, pp. 149–161. ACM, New York (2014)
Pietro, R.D., Me, G., Strangio, M.A.: A two-factor mobile authentication scheme for secure financial transactions. In: International Conference on Mobile Business (ICMB 2005), pp. 28–34, July 2005
Rövid, A., Szeid, L., Rudas, I., Várlaki, P.: Image processing on tensor-product basis. Obuda Univ. e-Bull. 2(1), 247–258 (2011)
Roy, A., Memon, N., Ross, A.: MasterPrint: exploring the vulnerability of partial fingerprint-based authentication systems. IEEE Trans. Inf. Forensics Secur. PP(99), 1 (2017)
Rybnicek, M., Lang-Muhr, C., Haslinger, D.: A roadmap to continuous biometric authentication on mobile devices. In: 2014 International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 122–127, August 2014
Silva, D.F., Batista, G.: Speeding up all-pairwise dynamic time warping matrix calculation, pp. 837–845
Song, C., Wang, A., Ren, K., Xu, W.: EyeVeri: a secure and usable approach for smartphone user authentication. In: IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9, April 2016
Tucker, L.R.: Some mathematical notes on three-mode factor analysis. Psychometrika 31(3), 279–311 (1966)
Welling, M., Weber, M.: Positive tensor factorization. Pattern Recogn. Lett. 22(12), 1255–1261 (2001)
Yi, S., Qin, Z., Novak, E., Yin, Y., Li, Q.: GlassGesture: exploring head gesture interface of smart glasses. In: IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9, April 2016
Zhu, H., Hu, J., Chang, S., Lu, L.: ShakeIn: secure user authentication of smartphones with habitual single-handed shakes. IEEE Trans. Mob. Comput. PP(99), 1 (2017)
