Continual retraining of keystroke dynamics based authenticator

Pilsung Kang, Seong Seob Hwang, Sungzoon Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

78 Citations (Scopus)


Keystroke dynamics based authentication (KDA) verifies a user based on the typing pattern. During enroll, a few typing patterns are provided, which are then used to train a classifier. The typing style of a user is not expected to change. However, sometimes it does change, resulting in a high false reject. In order to achieve a better authentication performance, we propose to continually retrain classifiers with recent login typing patterns by updating the training data set. There are two ways to update it. The moving window uses a fixed number of most recent patterns while the growing window uses all the new patterns as well as the original enroll patterns. We applied the proposed method to the real data set involving 21 users. The experimental results show that both the moving window and the growing window approach outperform the fixed window approach, which does not retrain a classifier.

Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2007, Proceedings
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783540745488
Publication statusPublished - 2007
Externally publishedYes
Event2007 International Conference on Advances in Biometrics, ICB 2007 - Seoul, Korea, Republic of
Duration: 2007 Aug 272007 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4642 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2007 International Conference on Advances in Biometrics, ICB 2007
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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