Enhanced password authentication through keystroke typing characteristics
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
The Effect of Clock Resolution on Keystroke Dynamics
RAID '08 Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection
A novel user-participating authentication scheme
Journal of Systems and Software
Intrusion detection and identification system using data mining and forensic techniques
IWSEC'07 Proceedings of the Security 2nd international conference on Advances in information and computer security
Keystroke dynamics in password authentication enhancement
Expert Systems with Applications: An International Journal
On the discriminability of keystroke feature vectors used in fixed text keystroke authentication
Pattern Recognition Letters
Identifying emotional states using keystroke dynamics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An intrusion detection technique based on continuous binary communication channels
International Journal of Security and Networks
Keystroke biometric system using wavelets
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
On the complexity of aggregating information for authentication and profiling
DPM'11 Proceedings of the 6th international conference, and 4th international conference on Data Privacy Management and Autonomous Spontaneus Security
Information Sciences: an International Journal
Examining a Large Keystroke Biometrics Dataset for Statistical-Attack Openings
ACM Transactions on Information and System Security (TISSEC)
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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%.