Pattern Classification Methods for Keystroke Identification

키스트로크 인식을 위한 패턴분류 방법

  • 조태훈 (한국기술교육대학교 정보기술공학부)
  • Published : 2006.05.01


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.


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