Keystroke Biometrics Ongoing Competition

This paper presents the first Keystroke Biometrics Ongoing Competition (KBOC) organized to establish a reproducible baseline in person authentication using keystroke biometrics. The competition has been developed using the BEAT platform and includes one of the largest keystroke databases publicly available based on a fixed text scenario. The database includes genuine and attacker keystroke sequences from 300 users acquired in four different sessions distributed in a four month time span. The sequences correspond to the user's name and surname, and therefore, each user comprises an individual and personal sequence. As baseline for KBOC, we report the results of 31 different algorithms evaluated according to accuracy and robustness. The systems have achieved EERs as low as 5.32% and high robustness to multisession variability with accuracy degradation lower than 1% for probes separated by months. The entire database is publicly available at the competition website.

[1]  Baochang Zhang,et al.  Study on the BeiHang Keystroke Dynamics Database , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[2]  Jugurta R. Montalvão Filho,et al.  Contributions to empirical analysis of keystroke dynamics in passwords , 2015, Pattern Recognit. Lett..

[3]  Sébastien Marcel,et al.  BEAT – biometrics evaluation and testing , 2013 .

[4]  John V. Monaco Robust Keystroke Biometric Anomaly Detection , 2016, ArXiv.

[5]  Javier Garrido Salas,et al.  BiosecurID: a multimodal biometric database , 2009, Pattern Analysis and Applications.

[6]  Bruce A. Draper,et al.  Overview of the Multiple Biometrics Grand Challenge , 2009, ICB.

[7]  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.

[8]  Julian Fiérrez,et al.  Adapted user-dependent multimodal biometric authentication exploiting general information , 2005, Pattern Recognit. Lett..

[9]  Christophe Rosenberger,et al.  Soft biometrics database: A benchmark for keystroke dynamics biometric systems , 2013, 2013 International Conference of the BIOSIG Special Interest Group (BIOSIG).

[10]  Margit Antal,et al.  Keystroke Dynamics on Android Platform , 2015 .

[11]  Erik Learned-Miller,et al.  Labeled Faces in the Wild : Updates and New Reporting Procedures , 2014 .

[12]  Jeffrey D. Allen,et al.  An analysis of pressure-based keystroke dynamics algorithms , 2010 .

[13]  Stephanie Schuckers,et al.  Shared research dataset to support development of keystroke authentication , 2014, IEEE International Joint Conference on Biometrics.

[14]  Roy A. Maxion,et al.  The Effect of Clock Resolution on Keystroke Dynamics , 2008, RAID.

[15]  Sharath Pankanti,et al.  Biometric Recognition: Security and Privacy Concerns , 2003, IEEE Secur. Priv..

[16]  Julian Fiérrez,et al.  Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics , 2014, ECCV Workshops.

[17]  Julian Fiérrez,et al.  Keystroke dynamics recognition based on personal data: A comparative experimental evaluation implementing reproducible research , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[18]  Julian Fiérrez,et al.  Score normalization for keystroke dynamics biometrics , 2015, 2015 International Carnahan Conference on Security Technology (ICCST).

[19]  Julian Fierrez,et al.  Aging in Biometrics: An Experimental Analysis on On-Line Signature , 2013, PloS one.

[20]  Julian Fiérrez,et al.  One-handed Keystroke Biometric Identification Competition , 2015, 2015 International Conference on Biometrics (ICB).

[21]  Davide Maltoni,et al.  Fingerprint verification competition at IJCB2011 , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[22]  Xiaoming Liu,et al.  On Continuous User Authentication via Typing Behavior , 2014, IEEE Transactions on Image Processing.

[23]  Meikang Qiu,et al.  Keystroke Biometric Systems for User Authentication , 2017, J. Signal Process. Syst..

[24]  Douglas A. Reynolds,et al.  Summary and initial results of the 2013-2014 speaker recognition i-vector machine learning challenge , 2014, INTERSPEECH.

[25]  Damon L. Woodard,et al.  Biometric Authentication and Identification using Keystroke Dynamics: A Survey , 2012 .

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

[27]  Yu Zhong,et al.  A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations , 2015 .

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

[29]  Julian Fiérrez,et al.  Target dependent score normalization techniques and their application to signature verification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[30]  Patrick Bours,et al.  Performance of keystroke dynamics when allowing typing corrections , 2014, 2nd International Workshop on Biometrics and Forensics.

[31]  G. Padmavathi,et al.  A Survey of Biometric keystroke Dynamics: Approaches, Security and Challenges , 2009, ArXiv.

[32]  Jugurta R. Montalvão Filho,et al.  KBOC: Keystroke biometrics OnGoing competition , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

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

[34]  Deian Stefan,et al.  Robustness of keystroke-dynamics based biometrics against synthetic forgeries , 2012, Comput. Secur..