title: A parallel decision tree-based method for user authentication based on keystroke patterns.
abstract: 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%.
authors.affiliation: Department of Electrical and Computer Engineering, Indiana University--Purdue University Indianapolis, Indianapolis, IN 46202-5132, USA. yosheng@iupui.edu
authors.forename: Yong
authors.initials: Y
authors.lastname: Sheng
authors.forename: Vir V
authors.initials: VV
authors.lastname: Phoha
authors.forename: Steven M
authors.initials: SM
authors.lastname: Rovnyak
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D016287:Computer Peripherals; D016494:Computer Security; D003661:Decision Support Techniques; D005385:Fingers / Q000502:physiology; D006801:Humans; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D013647:Task Performance and Analysis