A novel and simple statistical fusion method for user authentication through keystroke features

Pei Cheng Cheng, Ting Yi Chang, Cheng Jung Tsai, Jian Wei Li, Chih Sheng Wu

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

This paper utilizes a statistical fusion method to extract the characteristic information for key stroke based authentication (KA) systems to verify users' identities. The keystroke data is based on the time instances of pressing and releasing a key, and five features based on the time periods are calculated using these data. In the experiment, nineteen users participated as the legitimate users and each account was attacked by between 62 and 82 impostors. The average false acceptance rate is 1.035% and the false rejection rate is 0%. These rates are both competitive with other researches. The proposed method can be used as the classifier in password-based authentication systems to enhance their security.

原文English
頁(從 - 到)347-356
頁數10
期刊Journal of Convergence Information Technology
6
發行號2
DOIs
出版狀態Published - 2011 二月

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications

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