Adaptive Positive Selection for Keystroke Dynamics

Current technologies provide state of the art services but, at the same time, increase data exposure, mainly due to Internet-based applications. In view of this scenario, improved authentication mechanisms are needed. Keystroke dynamics, which recognizes users by their typing rhythm, is a cost-effective alternative. This technology usually only requires a common keyboard in order to acquire authentication data. There are several studies investigating the use of machine learning techniques for user authentication based on keystroke dynamics. However, the majority of them assume a scenario which the user model is not updated. It is known that typing rhythm changes over time (concept drift). Consequently, classification algorithms in keystroke dynamics have to be able to adapt the user model to these changes. This paper evaluates adaptation methods for an immune positive selection algorithm in a data stream context. Experimental results showed that they improved classification performance, mainly for false rejection rates.

[1]  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.

[2]  Bojan Cukic,et al.  Evaluating the Reliability of Credential Hardening through Keystroke Dynamics , 2006, 2006 17th International Symposium on Software Reliability Engineering.

[3]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[4]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[5]  Heather Crawford.  Keystroke dynamics: Characteristics and opportunities , 2010, 2010 Eighth International Conference on Privacy, Security and Trust.

[6]  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).

[7]  Baptiste Hemery,et al.  Unconstrained keystroke dynamics authentication with shared secret , 2011, Comput. Secur..

[8]  Gian Luca Marcialis,et al.  Adaptive Biometric Systems That Can Improve with Use , 2008 .

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

[10]  Andrey Makrushin,et al.  Automatic Template Update Strategies for Biometrics , 2007 .

[11]  Christophe Rosenberger,et al.  Hybrid template update system for unimodal biometric systems , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[12]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

[13]  Christophe Rosenberger,et al.  Can Chronological Information be Used as a Soft Biometric in Keystroke Dynamics? , 2012, 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[14]  Gian Luca Marcialis,et al.  Self adaptive systems: An experimental analysis of the performance over time , 2011, 2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM).

[15]  Gian Luca Marcialis,et al.  Semi-supervised PCA-Based Face Recognition Using Self-training , 2006, SSPR/SPR.

[16]  Christophe Rosenberger,et al.  Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis , 2012, 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[17]  Gian Luca Marcialis,et al.  Replacement Algorithms for Fingerprint Template Update , 2008, ICIAR.

[18]  Christophe Rosenberger,et al.  Analysis of template update strategies for keystroke dynamics , 2011, 2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM).

[19]  Claudia Eckert,et al.  Is negative selection appropriate for anomaly detection? , 2005, GECCO '05.

[20]  Christophe Rosenberger,et al.  Performance Evaluation of Biometric Template Update , 2012, ArXiv.

[21]  Sungzoon Cho,et al.  Continual Retraining of Keystroke Dynamics Based Authenticator , 2007, ICB.

[22]  F. Azuaje.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[23]  Xian Ke,et al.  Typing patterns: a key to user identification , 2004, IEEE Security & Privacy Magazine.

[24]  Roy A. Maxion,et al.  Why Did My Detector Do That?! - Predicting Keystroke-Dynamics Error Rates , 2010, RAID.

[25]  Sungzoon Cho,et al.  Novelty Detection Approach for Keystroke Dynamics Identity Verification , 2003, IDEAL.

[26]  Ana Carolina Lorena,et al.  A systematic review on keystroke dynamics , 2013, Journal of the Brazilian Computer Society.

[27]  K. Revett,et al.  Password secured sites - stepping forward with keystroke dynamics , 2005, International Conference on Next Generation Web Services Practices (NWeSP'05).

[28]  Andrew Beng Jin Teoh,et al.  A Survey of Keystroke Dynamics Biometrics , 2013, TheScientificWorldJournal.

[29]  Wahyudi,et al.  Keystroke Pressure-Based Typing Biometrics Authentication System Using Support Vector Machines , 2007, ICCSA.

[30]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[31]  Sahin Albayrak,et al.  Continuous and non-intrusive identity verification in real-time environments based on free-text keystroke dynamics , 2011, 2011 International Joint Conference on Biometrics (IJCB).