DocumentCode :
2126747
Title :
Keystroke Biometric Studies on Password and Numeric Keypad Input
Author :
Bakelman, Ned ; Monaco, John V. ; Sung-Hyuk Cha ; Tappert, Charles C.
Author_Institution :
Seidenberg Sch. of Comput. Sci. & Inf. Syst., Pace Univ., White Plains, NY, USA
fYear :
2013
fDate :
12-14 Aug. 2013
Firstpage :
204
Lastpage :
207
Abstract :
The keystroke biometric classification system described in this study was evaluated on two types of short input - passwords and numeric keypad input. On the password input, the system outperforms 14 other systems evaluated in a previous study using the same raw input data. The three top performing systems in that study had equal error rates between 9.6% and 10.2%. With the classification system developed in this study, equal error rates of 8.7% were achieved on both the features from the previous study and on a new set of features. On the numeric keypad input, the system achieved an equal error rate of 10.5% on the features from the previous study and 6.1% on a new set of features.
Keywords :
biometrics (access control); message authentication; pattern classification; equal error rates; keystroke biometric classification system; keystroke biometric studies; numeric keypad input; password input; Authentication; Biometrics (access control); Educational institutions; Error analysis; Sociology; Statistics; biometrics; keystroke biometrics; machine learning; pattern recognition; user authentication;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligence and Security Informatics Conference (EISIC), 2013 European
Conference_Location :
Uppsala
Type :
conf
DOI :
10.1109/EISIC.2013.45
Filename :
6657155
Link To Document :
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