Keystroke statistical learning model for web authentication

Cheng Huang Jiang*, Shiuhpyng Shieh, Jen Chien Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations

Abstract

Keystroke typing characteristics is considered as one of the important biometric features that can be used to protect users against malicious attacks. In this paper we propose a statistical model for web authentication with keystroke typing characteristics based on Hidden Markov Model and Gaussian Modeling from Statistical Learning Theory. Our proposed model can substantially enhance the accuracy of the identity authentication by analyzing keystroke timing information of the username and password. Results of the experiments showed that our scheme achieved by far the best error rate of 2.54%.

Original languageEnglish
Title of host publicationProceedings of the 2nd ACM Symposium on Information, Computer and Communications Security, ASIACCS '07
Pages359-361
Number of pages3
DOIs
StatePublished - 1 Oct 2007
Event2nd ACM Symposium on Information, Computer and Communications Security, ASIACCS '07 - Singapore, Singapore
Duration: 20 Mar 200722 Mar 2007

Publication series

NameProceedings of the 2nd ACM Symposium on Information, Computer and Communications Security, ASIACCS '07

Conference

Conference2nd ACM Symposium on Information, Computer and Communications Security, ASIACCS '07
CountrySingapore
CitySingapore
Period20/03/0722/03/07

Keywords

  • Gaussian model
  • Hidden Markov model
  • Keystroke
  • Statistical learning theory
  • Web authentication

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  • Cite this

    Jiang, C. H., Shieh, S., & Liu, J. C. (2007). Keystroke statistical learning model for web authentication. In Proceedings of the 2nd ACM Symposium on Information, Computer and Communications Security, ASIACCS '07 (pp. 359-361). (Proceedings of the 2nd ACM Symposium on Information, Computer and Communications Security, ASIACCS '07). https://doi.org/10.1145/1229285.1229327