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Invisible, Passive, Continuous and Multimodal Authentication

  • Karen Renaud
  • Heather Crawford
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8045)

Abstract

Authentication, as traditionally achieved by means of a shared secret, is effortful and deliberate. Frequent and repeated authentication easily becomes a hurdle, an annoyance and a burden. This state of affairs needs to be addressed, and one of the ways of doing this is by moving towards automating the process as much as possible, and reducing the associated effort — ie. reducing its visibility. A shared secret clearly does not have the flexibility to support this, and we need therefore to consider using biometrics. Biometrics are a well-established authentication method. Physiological biometrics require a biometric reader and explicit action by the user. Furthermore, there are always a minority of users who cannot have a particular biometric measured. For example elderly women often lose their fingerprints, and iris biometrics don’t work for people with particular eye conditions.Behavioural biometrics, however, can be collected without the user having to take deliberate action. Hence there is a strong possibility that these biometrics could deliver the invisible and automatic authentication we are striving towards. One big advantage of these biometrics is that, since there is no reader, it is simple to utilise a number of different biometrics, and to combine these to authenticate the user. If one biometric fails the others can still perform authentication.

Here we propose using patterns such as keystroke dynamics, use patterns, and voice analysis techniques to create a multimodal biometric authentication mechanism. These behavioural biometrics take advantage of tasks that the user already performs thereby reducing the need for explicit authentication by more traditional means. In this way, the user is relieved of the burdens of constantly authenticating to multiple applications and devices.

Keywords

Shared Secret Biometric System Voice Analysis Keystroke Dynamic Multimodal Biometric System 
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.

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References

  1. 1.
    Ahmed, A.A.E., Traore, I., Almulhem, A.: Digital Fingerprinting Based on Keystroke Dynamics. In: Proceedings of the Second International Symposium on Human Aspects of Information Security & Assurance (HAISA 2008), Plymouth, UK, pp. 94–104 (July 2008)Google Scholar
  2. 2.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)Google Scholar
  3. 3.
    Bonastre, J.-F., Bimbot, F., Boe, L.-J., Cambell, J.P., Reynolds, D.A., Magrin-Chagnolleau, I.: Person Authentication by Voice: A Need for Caution. In: Proceedings of Eurospeech 2003 (2003)Google Scholar
  4. 4.
    Brunelli, R., Falavigna, D.: Person Identification Using Multiple Cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(10), 955–966 (1995)CrossRefGoogle Scholar
  5. 5.
    Buchoux, A., Clarke, N.L.: Deployment of Keystroke Analysis on a Smartphone. In: Proceedings of the 6th Australian Information Security Management Conference, Perth, Western Australia, pp. 40–47. SECAU - Security Research Centre (2008)Google Scholar
  6. 6.
    Cho, S., Han, C., Han, D.H., Kim, H.-I.: Web based Keystroke Dynamics Identity Verification Using Neural Network. Journal of Organizational Computing and Electronic Commerce 10(4), 295–307 (2000)CrossRefGoogle Scholar
  7. 7.
    Clarke, N.L., Furnell, S.M., Reynolds, P.L.: Biometric Authentication for Mobile Devices. In: Proceedings of the 3rd Australian Information Warfare and Security Conference 2002, pp. 61–69 (2002)Google Scholar
  8. 8.
    Clarke, N.L., Furnell, S.M.: Authenticating Mobile Phone Users Using Keystroke Analysis. International Journal of Information Security 6(1), 1–14 (2007)CrossRefGoogle Scholar
  9. 9.
    Garcia-Salicetti, S., Beumier, C., Chollet, G., Dorizzi, B., Leroux les Jardins, J., Lunter, J., Ni, Y., Petrovska-Delacretaz, D.: BIOMET: A Multimodal Person Authentication Database Including Face, Voice, Fingerprint, Hand and Signature Modalities. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 845–853. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Gunetti, D., Picardi, C.: Keystroke Analysis of Free Text. ACM Transactions on Information and System Security 8(3), 312–347 (2005)CrossRefGoogle Scholar
  11. 11.
    Hirschman, L.: Multi-Site Data Collection for a Spoken Language Corpus. In: Proceedings of the Workshop on Speech and Natural Language, pp. 7–14. ACM (1992)Google Scholar
  12. 12.
    Iwano, K., Hirose, T., Kamibayashi, E., Furui, S.: Audio-Visual Person Authentication Using Speech and Ear Images. In: Proceedings of Workshop on Multimodal User Authentication, pp. 85–90 (2003)Google Scholar
  13. 13.
    Jain, A.K., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 4–20 (2004)CrossRefGoogle Scholar
  14. 14.
    Karatzouni, S., Clarke, N.: Keystroke Analysis for Thumb-based Keyboards on Mobile Devices. In: Venter, H., Eloff, M., Labuschagne, L., Eloff, J., von Solms, R. (eds.) New Approaches for Security, Privacy and Trust in Complex Environments. IFIP, vol. 232, pp. 253–263. Springer, Boston (2007)CrossRefGoogle Scholar
  15. 15.
    Obaidat, M.S., Sadoun, B.: Verification of Computer Users Using Keystroke Dynamics. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 27(2), 261–269 (1997)CrossRefGoogle Scholar
  16. 16.
    Price Waterhouse Coopers. Information security breaches survey 2004. Technical report, Department of Trade and Industry (2004)Google Scholar
  17. 17.
    Psathas, G.: Conversation Analysis: The Study of Talk-in-Interaction. Sage Publications (1995)Google Scholar
  18. 18.
    Ross, A., Jain, A.K.: Multimodal Biometrics: An Overview. In: Proceedings of the 12th European Signal Processing Conference (EUSIPCO), pp. 1221–1224 (September 2004)Google Scholar
  19. 19.
    Saevanee, H., Bhattarakosol, P.: Authenticating User Using Keystroke Dynamics and Finger Pressure. In: Proceedings of the 6th IEEE Consumer Communications and Networking Conference, Las Vegas, NV, USA, pp. 1–2. IEEE (2009)Google Scholar
  20. 20.
    Smith, R.E.: Authentication: From Passwords to Public Keys. Addison-Wesley (2002)Google Scholar
  21. 21.
    Spillane, R.: Keyboard Apparatus for Personal Identification. Technical Report 17, IBM Technical Disclosure Bulletin (1975)Google Scholar
  22. 22.
    Taussig, K., Bernstein, J.: Macrophone: An American English Telephone Speech Corpus. In: Proceedings of the Workshop on Human Language Technology, Plainsboro, NJ, USA, pp. 27–30. ACM (1994)Google Scholar
  23. 23.
    Vinciarelli, A.: Speakers Role Recognition in Multiparty Audio Recordings Using Social Network Analysis and Duration Distribution Meeting. IEEE Transactions on Multimedia 9(6), 1215–1226 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Karen Renaud
    • 1
  • Heather Crawford
    • 1
  1. 1.School of Computing ScienceUniversity of GlasgowGlasgowUK

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