Pace University's Keystroke Biometric System (PKBS)

Over the last eight years Seidenberg's School of CSIS at Pace University has developed robust text-input keystroke biometric systems for identification (one-of-n response) and for authentication (accept/reject response). As far as we can determine, these text-input systems are currently the best in existence and we strongly desire to maintain our leadership in this area. While there are several commercial keystroke biometric systems that operate on passwords, a few seconds of input, our systems operate on arbitrary text input of longer duration, in minutes, and can therefore capture more robust statistical-based features. We have also worked in the related computer input areas of mouse activity, stylometry (forensic authorship), and semantic operational biometrics. We have presented experimental results at conferences and have recently published a book chapter and journal article.

There are several interesting applications of this work with related funding opportunities. The Department of Defense through DARPA's "Active Authentication" project wants to detect intruders by continually authenticating users of all government machines. NIST is interested in verifying the identity of customers making online transactions. And the 2008 United States Higher Education Opportunity Act requires institutions of higher learning to make greater online access control efforts by adopting ubiquitous identification technologies, for example to verify the identity of students taking online tests. While all three applications are similar in terms of authenticating the user, faster discovery is required in the first two to prevent significant harm.

In 2013 a major system improvement [1] dropped our authentication system error rate by a factor of five over those reported in reference [2]. The improved system results on a population of 120 users yielded a biometric performance rate of over 99% for an input of 100 or more keystrokes (100 keystrokes is about 15 words).

Primary references
  1. J.V. Monaco, N. Bakelman, S. Cha, and C.C. Tappert, "Recent Advances in the Development of a Keystroke Biometric Authentication System for Long-Text Input," unpublished. PDF
  2. J.V. Monaco, N. Bakelman, S. Cha, and C.C. Tappert, "Developing a Keystroke Biometric System for Continual Authentication of Computer Users," Proc. 2012 European Intelligence and Security Informatics Conference, Denmark, August 2012. PDF   Slides
  3. J.C. Stewart, J.V. Monaco, S. Cha, and C.C. Tappert, "An Investigation of Keystroke and Stylometry Traits for Authenticating Online Test Takers," Proc. IEEE Int. Joint Conf. Biometrics, Washington D.C., October 2011. PDF
  4. C.C. Tappert, S. Cha, M. Villani, and R.S. Zack, "Keystroke Biometric Identification and Authentication on Long-Text Input," Int. Journal Information Security and Privacy (IJISP), 2010. PDF
  5. R.S. Zack, C.C. Tappert, and S. Cha, "Performance of a Long-Text-Input Keystroke Biometric Authentication System Using an Improved k-Nearest-Neighbor Classification Method," Proc. IEEE 4th Int. Conf. Biometrics, Washington D.C., September 2010. PDF   Slides
  6. C.C. Tappert, M. Villani, and S. Cha, "Keystroke Biometric Identification and Authentication on Long-Text Input," pp 342-367, Chapter 16 in Behavioral Biometrics for Human Identification: Intelligent Applications, Edited by Liang Wang and Xin Geng, Medical Information Science Reference, 2010. PDF
  7. M. Villani, C.C. Tappert, G. Ngo, J. Simone, H. St. Fort, and S. Cha, "Keystroke Biometric Recognition Studies on Long-Text Input under Ideal and Application-Oriented Conditions," Proc. CVPR 2006 Workshop on Biometrics, New York, NY, June 2006. PDF