On the equalization of keystroke timing histograms
Pattern Recognition Letters
Automated stress detection using keystroke and linguistic features: An exploratory study
International Journal of Human-Computer Studies
Keystroke dynamics with low constraints SVM based passphrase enrollment
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Why did my detector do that?!: predicting keystroke-dynamics error rates
RAID'10 Proceedings of the 13th international conference on Recent advances in intrusion detection
On the discriminability of keystroke feature vectors used in fixed text keystroke authentication
Pattern Recognition Letters
Retraining a novelty detector with impostor patterns for keystroke dynamics-based authentication
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Biometric access control through numerical keyboards based on keystroke dynamics
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
GA SVM wrapper ensemble for keystroke dynamics authentication
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Journal of Systems and Software
User authentication via keystroke dynamics based on difference subspace and slope correlation degree
Digital Signal Processing
Gait verification using knee acceleration signals
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Keystroke timing analysis of on-the-fly web apps
ACNS'13 Proceedings of the 11th international conference on Applied Cryptography and Network Security
Expert Systems with Applications: An International Journal
Keystroke biometrics: the user perspective
Proceedings of the 4th ACM conference on Data and application security and privacy
Hi-index | 35.69 |
This paper uses a static keystroke dynamics in user authentication. The inputs are the key down and up times and the key ASCII codes captured while the user is typing a string. Four features (key code, two keystroke latencies, and key duration) were analyzed and seven experiments were performed combining these features. The results of the experiments were evaluated with three types of user: the legitimate, the impostor and the observer impostor users. The best results were achieved utilizing all features, obtaining a false rejection rate of 1.45% and a false acceptance rate of 1.89%. This approach can be used to improve the usual login-password authentication when the password is no more a secret. This paper innovates using four features to authenticate users.