密码和数字键盘输入的击键生物识别研究

Ned Bakelman, John V. Monaco, Sung-Hyuk Cha, C. Tappert
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引用次数: 26

摘要

本研究描述的按键生物识别分类系统在两种类型的短输入-密码和数字键盘输入上进行了评估。在密码输入方面,该系统优于先前使用相同原始输入数据的研究中评估的14个其他系统。该研究中表现最好的三个系统的错误率在9.6%到10.2%之间。使用本研究开发的分类系统,在之前研究的特征和一组新的特征上都实现了8.7%的错误率。在数字键盘输入上,系统在前一项研究的特征上的错误率为10.5%,在一组新特征上的错误率为6.1%。
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Keystroke Biometric Studies on Password and Numeric Keypad Input
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.
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