Abstract
Handheld devices today do not continuously verify the identity of the user while sensitive activities are performed. This enables attackers, who can either compromise the initial password or grab the device after login, full access to sensitive data and applications on the device. To mitigate this risk, we propose continuous user monitoring using a machine learning based approach comprising of an ensemble of three distinct modalities: power consumption, touch gestures, and physical movement. Users perform different activities on different applications: we consider application context when we model user behavior. We employ anomaly detection algorithms for each modality and place a bound on the fraction of anomalous events that can be considered “normal” for any given user. We evaluated our system using data collected from 73 volunteer participants. We were able to verify that our system is functional in real-time while the end-user was utilizing popular mobile applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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, pp. 1–7. USENIX Association (2010)
Muslukhov, I., Boshmaf, Y., Kuo, C., Lester, J., Beznosov, K.: Know your enemy: the risk of unauthorized access in smartphones by insiders. In: Proceedings of the 15th International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 271–280. ACM (2013)
Karlson, A.K., Brush, A.J., Schechter, S.: Can i borrow your phone?: understanding concerns when sharing mobile phones. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1647–1650. ACM (2009)
Clarke, N.L., Furnell, S.M.: Advanced user authentication for mobile devices. Comput. Secur. 26, 109–119 (2007)
Shi, E., Niu, Y., Jakobsson, M., Chow, R.: Implicit authentication through learning user behavior. In: Burmester, M., Tsudik, G., Magliveras, S., Ilić, I. (eds.) ISC 2010. LNCS, vol. 6531, pp. 99–113. Springer, Heidelberg (2011)
Riva, O., Qin, C., Strauss, K., Lymberopoulos, D.: Progressive authentication: deciding when to authenticate on mobile phones. In: Proceedings of the 21st USENIX Security Symposium (2012)
Frank, M., Biedert, R., Ma, E., Martinovic, I., Song, D.: Touchalytics: on the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Trans. Inf. Forensics Secur. 8, 136–148 (2013)
Bo, C., Zhang, L., Jung, T., Han, J., Li, X.-Y., Wang, Y.: Continuous user identification via touch and movement behavioral biometrics. In: 2014 IEEE International Conference on Performance Computing and Communications (IPCCC), pp. 1–8. IEEE (2014)
Yampolskiy, R.V., Govindaraju, V.: Behavioural biometrics: a survey and classification. Int. J. Biometrics 1, 81–113 (2008)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Cell phone-based biometric identification. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–7. IEEE (2010)
Killourhy, K.S., Maxion, R.A.: Comparing anomaly-detection algorithms for keystroke dynamics. In: 2009 IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2009, pp. 125–134. IEEE (2009)
Shen, C., Cai, Z., Maxion, R.A., Xiang, G., Guan, X.: Comparing classification algorithm for mouse dynamics based user identification. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 61–66 (2012)
Zhang, L., Tiwana, B., Qian, Z., Wang, Z., Dick, R.P., Mao, Z.M., Yang, L.: Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: Proceedings of the Eighth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, pp. 105–114. ACM (2010)
Murmuria, R., Medsger, J., Stavrou, A., Voas, J.M.: Mobile application and device power usage measurements. In: 2012 IEEE Sixth International Conference on Software Security and Reliability (SERE), pp. 147–156 (2012)
Shye, A., Scholbrock, B., Memik, G.: Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures. In: Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture, pp. 168–178. ACM (2009)
Barbará, D., Domeniconi, C., Rogers, J.P.: Detecting outliers using transduction and statistical testing. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 55–64. ACM (2006)
Keogh, E., Lin, J., Fu, A.: Hot sax: efficiently finding the most unusual time series subsequence. In: Fifth IEEE International Conference on Data Mining. IEEE (2005)
Vovk, V., Gammerman, A., Saunders, C.: Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), pp. 444–453 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Murmuria, R., Stavrou, A., Barbará, D., Fleck, D. (2015). Continuous Authentication on Mobile Devices Using Power Consumption, Touch Gestures and Physical Movement of Users. In: Bos, H., Monrose, F., Blanc, G. (eds) Research in Attacks, Intrusions, and Defenses. RAID 2015. Lecture Notes in Computer Science(), vol 9404. Springer, Cham. https://doi.org/10.1007/978-3-319-26362-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-26362-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26361-8
Online ISBN: 978-3-319-26362-5
eBook Packages: Computer ScienceComputer Science (R0)