Notice of Violation of IEEE Publication Principles: Keystroke Biometrics with number-pad input using hybridization of adaboost with random forest

Notice of Violation of IEEE Publication Principles

“Keystroke Biometrics with Number-Pad Input Using Hybridization of Adaboost with Random Forest”
by N. Eswari, S. Sundarapandiyan, P. Vennila, R. Umamaheswari, and G. Jothilakshmi in the Proceedings of the IEEE International Conference On Advances in Engineering, Science And Management (ICAESM), March 2012, pp.105-109. , BR>
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper duplicates large portions of content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

“Keystroke Biometrics with Number-Pad Input”
by Roy A. Maxion and Kevin S. Killourhy
in the Proceedings of the IEEE/IFIP International Conference on Dependable Systems & Networks (DSN), 2010, pp.201-210


Keystroke Biometrics is a new authentication technique to identify legitimate users via their typing behavior, which are in turn derived from the timestamps of key-press and keyrelease events in the keyboard while typing their password. Many researchers have explored this domain, with mixed results, but few have examined the relatively impoverished results for digits only password, so that the input password is from the number-pad portion of the keyboard. In this paper, machine learning technique is adapted for keystroke authentication. The selected classification method is adaboost and random forest. Also, combination of adaboost and Random forest will improve the accuracy of the system. The performance metrics are FAR (False Acceptance Rate) and FRR (False Rejection Rate).