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Efficient Intrusion Detection for Mobile Devices Using Spatio-temporal Mobility Patterns

  • Sausan Yazji
  • Robert P. Dick
  • Peter Scheuermann
  • Goce Trajcevski
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 73)

Abstract

Mobile phones are ubiquitous and are used for email, text messages, navigation, education, and as a pyment tool (e.g., Mobile Money - extensively used in China and Japan [1]). Consequently, mobile devices carry a lot of personal data and, if stolen, that data can be more important than the loss of the device.

Most of the works on mobile devices security have focused on physical aspects and/or access control, which do not protect the private data on a stolen device that is in the post authentication state. However, some existing works, e.g. Laptop Cop [2] aim to protect data on stolen devices by remotely and manually deleting it, which requires user intervention. It may take hours before the user notices the loss of his device.

Keywords

Mobile Device Intrusion Detection Anomaly Detection Information Management System Mobile Payment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Chen, L.-D.: A Model of Consumer Acceptance of Mobile Payment. J. IJMC 6(1), 32–52 (2008)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Yazji, S., Chen, X., Dick, R.P., Scheuermann, P.: Implicit User Re-authentication for Mobile Devices. In: Zhang, D., Portmann, M., Tan, A.-H., Indulska, J. (eds.) UIC 2009. LNCS, vol. 5585, pp. 325–339. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Sun, B., Yu, F., Wu, K., Xiao, Y., Leung, V.: Enhancing Security Using Mobility-Based Anomaly Detection in Cellular Mobile Networks. IEEE Trans. Vehicular Technology (2007)Google Scholar
  5. 5.
    Hall, J., Barbeau, M., Kranakis, E.: Anomaly-Based Intrusion Detection Using Mobility Profiles of Public Transportation Users. In: Proc. WiMob (2005)Google Scholar
  6. 6.
    Yan, G., Eidenbenz, S., Sun, B.: Mobi-Watchdog: You Can Steal, But You Can’t Run! In: Proc. WiSec (2009)Google Scholar
  7. 7.
    Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding Individual Human Mobility Patterns. J. Nature 453 (2008)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Sausan Yazji
    • 1
  • Robert P. Dick
    • 2
  • Peter Scheuermann
    • 1
  • Goce Trajcevski
    • 1
  1. 1.EECS Dept.Northwestern UniversityEvanstonUSA
  2. 2.EECS Dept.University of MichiganAnn ArborUSA

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