A parallel decision tree-based method for user authentication based on keystroke patterns

We propose a Monte Carlo approach to attain sufficient training data, a splitting method to improve effectiveness, and a system composed of parallel decision trees (DTs) to authenticate users based on keystroke patterns. For each user, approximately 19 times as much simulated data was generated to c...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2005-08, Vol.35 (4), p.826-833
Main Authors: Yong Sheng, Phoha, V.V., Rovnyak, S.M.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Authentication
Biometrics
Biometry - methods
Computational modeling
Computer Peripherals
Computer Security
Computer simulation
Cybernetics
Data security
Decision Support Techniques
Decision trees
Fingers - physiology
Humans
keystroke patterns
Mathematical analysis
Monte Carlo methods
Movement - physiology
network security
parallel decision trees
Pattern Recognition, Automated - methods
Raw
simulations
Strings
Studies
Task Performance and Analysis
Testing
Training
Training data
wavelet analysis
Wavelet transforms
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Summary:We propose a Monte Carlo approach to attain sufficient training data, a splitting method to improve effectiveness, and a system composed of parallel decision trees (DTs) to authenticate users based on keystroke patterns. For each user, approximately 19 times as much simulated data was generated to complement the 387 vectors of raw data. The training set, including raw and simulated data, is split into four subsets. For each subset, wavelet transforms are performed to obtain a total of eight training subsets for each user. Eight DTs are thus trained using the eight subsets. A parallel DT is constructed for each user, which contains all eight DTs with a criterion for its output that it authenticates the user if at least three DTs do so; otherwise it rejects the user. Training and testing data were collected from 43 users who typed the exact same string of length 37 nine consecutive times to provide data for training purposes. The users typed the same string at various times over a period from November through December 2002 to provide test data. The average false reject rate was 9.62% and the average false accept rate was 0.88%.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2005.846648