Abstract

Keystroke time intervals can be a discriminating feature in the verification and identification of computer users. This paper presents a comparison result obtained using several classification methods including k-NN (k-Nearest Neighbor), back-propagation neural networks, and Bayesian classification for keystroke identification. Performance of k-NN classification was best with small data samples available per user, while Bayesian classification was the most superior to others with large data samples per user. Thus, for web-based on-line identification of users, it seems to be appropriate to selectively use either k-NN or Bayesian method according to the number of keystroke samples accumulated by each user.

Keywords

• keystroke analysis;
• k-nearest neighbor;
• neural network;
• Bayesian classifier

File

Download PDF

References

1. R. Gaines, W. Lisowski, S. Press, and N. Shapiro, 'Authentication by Keystroke timing: some preliminary results,' Rand Report R-256-NSF, Rand Corporation, 1980
2. J. Leggett, G. Williams, 'Verifying identity via keystroke characteristics,' Int. J. Man-Mach. Stud. vol.28, no.1, pp. 67-76,1988 https://doi.org/10.1016/S0020-7373(88)80053-1
3. J. Leggett, G. Williams, D. Umphress, 'Verification of user identity via keystroke characteristics,' Human Factors in management Information System, Ablex Publishing Corp., Norwood, NJ, 1988
4. T. Ord, S.M. Furnell, 'User