Keystroke Biometric Recognition Studies on Long-Text Input under Ideal and Application-Oriented Conditions

Authors:
Mary Villani;Charles Tappert;Giang Ngo;Justin Simone;Huguens St. Fort;Sung-Hyuk Cha
Affiliations:
Pace University, USA;Pace University, USA;Pace University, USA;Pace University, USA;Pace University, USA;Pace University, USA
Venue:
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Year:
2006

Citing 0
Cited 7

Fuzzy and Markov models for keystroke biometrics authentication

SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Detecting cognitive and physical stress through typing behavior

CHI '09 Extended Abstracts on Human Factors in Computing Systems
Automated stress detection using keystroke and linguistic features: An exploratory study

International Journal of Human-Computer Studies
Homogeneous physio-behavioral visual and mouse-based biometric

ACM Transactions on Computer-Human Interaction (TOCHI)
Keystroke dynamics in a general setting

ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
A Keystroke Biometric Systemfor Long-Text Input

International Journal of Information Security and Privacy
Different strokes for different folks: individual stress response as manifested in typed text

CHI '13 Extended Abstracts on Human Factors in Computing Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A long-text-input keystroke biometric system was developed for applications such as identifying perpetrators of inappropriate e-mail or fraudulent Internet activity. A Java applet collected raw keystroke data over the Internet, appropriate long-text-input features were extracted, and a pattern classifier made identification decisions. Experiments were conducted on a total of 118 subjects using two input modes - copy and free-text input - and two keyboard types - desktop and laptop keyboards. Results indicate that the keystroke biometric can accurately identify an individual who sends inappropriate email (free text) if sufficient enrollment samples are available and if the same type of keyboard is used to produce the enrollment and questioned samples. For laptop keyboards we obtained 99.5% accuracy on 36 users, which decreased to 97.9% on a larger population of 47 users. For desktop keyboards we obtained 98.3% accuracy on 36 users, which decreased to 93.3% on a larger population of 93 users. Accuracy decreases significantly when subjects used different keyboard types or different input modes for enrollment and testing.