Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis

Most keystroke dynamics studies have been evaluated using a specific kind of dataset in which users type an imposed login and password. Moreover, these studies are optimistics since most of them use different acquisition protocols, private datasets, controlled environment, etc. In order to enhance the accuracy of keystroke dynamics' performance, the main contribution of this paper is twofold. First, we provide a new kind of dataset in which users have typed both an imposed and a chosen pairs of logins and passwords. In addition, the keystroke dynamics samples are collected in a web-based uncontrolled environment (OS, keyboards, browser, etc.). Such kind of dataset is important since it provides us more realistic results of keystroke dynamics' performance in comparison to the literature (controlled environment, etc.). Second, we present a statistical analysis of well known assertions such as the relationship between performance and password size, impact of fusion schemes on system overall performance, and others such as the relationship between performance and entropy. We put into obviousness in this paper some new results on keystroke dynamics in realistic conditions.

[1]  Norman Shapiro,et al.  Authentication by Keystroke Timing: Some Preliminary Results , 1980 .

[2]  Jean-Yves Ramel,et al.  Estimation of User Specific Parameters in One-class Problems , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[3]  Roy A. Maxion,et al.  Comparing anomaly-detection algorithms for keystroke dynamics , 2009, 2009 IEEE/IFIP International Conference on Dependable Systems & Networks.

[4]  Roy A. Maxion,et al.  Keystroke biometrics with number-pad input , 2010, 2010 IEEE/IFIP International Conference on Dependable Systems & Networks (DSN).

[5]  J. J. Higgins.  Introduction to Modern Nonparametric Statistics , 2003 .

[6]  Kenneth Revett,et al.  Developing a keystroke dynamics based agent using rough sets , 2005 .

[7]  Stergios B. Fotopoulos,et al.  Introduction to Modern Nonparametric Statistics , 2004, Technometrics.

[8]  Danoush Hosseinzadeh,et al.  Gaussian Mixture Modeling of Keystroke Patterns for Biometric Applications , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Christophe Rosenberger,et al.  A new soft biometric approach for keystroke dynamics based on gender recognition , 2012, Int. J. Inf. Technol. Manag..

[10]  Asok Ray,et al.  On the discriminability of keystroke feature vectors used in fixed text keystroke authentication , 2011, Pattern Recognit. Lett..

[11]  George Gabla.  Applying Keystroke Dynamics for Personal Authentication , 2005 .

[12]  Sungzoon Cho,et al.  Web-Based Keystroke Dynamics Identity Verification Using Neural Network , 2000, J. Organ. Comput. Electron. Commer..

[13]  Christophe Rosenberger,et al.  GREYC keystroke: A benchmark for keystroke dynamics biometric systems , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[14]  Shiuh-Pyng Shieh,et al.  Keystroke statistical learning model for web authentication , 2007, ASIACCS '07.

[15]  Christophe Rosenberger,et al.  Keystroke dynamics with low constraints SVM based passphrase enrollment , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[16]  Andrew Beng Jin Teoh,et al.  Statistical Fusion Approach on Keystroke Dynamics , 2007, 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System.

[17]  Kenneth Revett,et al.  A machine learning approach to keystroke dynamics based user authentication , 2007, Int. J. Electron. Secur. Digit. Forensics.

[18]  Victor Adolfsson,et al.  Applying Keystroke Dynamics for Personal Authentication , 2005 .