Share Email Print
cover

Proceedings Paper

The physiology of keystroke dynamics
Author(s): Jeffrey Jenkins; Quang Nguyen; Joseph Reynolds; William Horner; Harold Szu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

A universal implementation for most behavioral Biometric systems is still unknown since some behaviors aren't individual enough for identification. Habitual behaviors which are measurable by sensors are considered 'soft' biometrics (i.e., walking style, typing rhythm), while physical attributes (i.e., iris, fingerprint) are 'hard' biometrics. Thus, biometrics can aid in the identification of a human not only in cyberspace but in the world we live in. Hard biometrics have proven to be a rather successful form of identification, despite a large amount of individual signatures to keep track of. Virtually all soft biometric strategies, however, share a common pitfall. Instead of the classical pass/fail decision based on the measurements used by hard biometrics, a confidence threshold is imposed, increasing False Alarm and False Rejection Rates. This unreliability is a major roadblock for large scale system integration. Common computer security requires users to log-in with a six or more digit PIN (Personal Identification Number) to access files on the disk. Commercially available Keystroke Dynamics (KD) software can separately calculate and keep track of the mean and variance for each time travelled between each key (air time), and the time spent pressing each key (touch time). Despite its apparent utility, KD is not yet a robust, fault-tolerant system. We begin with a simple question: how could a pianist quickly control so many different finger and wrist movements to play music? What information, if any, can be gained from analyzing typing behavior over time? Biology has shown us that the separation of arm and finger motion is due to 3 long nerves in each arm; regulating movement in different parts of the hand. In this paper we wish to capture the underlying behavioral information of a typist through statistical memory and non-linear dynamics. Our method may reveal an inverse Compressive Sensing mapping; a unique individual signature.

Paper Details

Date Published: 8 June 2011
PDF: 10 pages
Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80581N (8 June 2011); doi: 10.1117/12.887419
Show Author Affiliations
Jeffrey Jenkins, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Quang Nguyen, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Joseph Reynolds, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
William Horner, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Harold Szu, U.S. Army Night Vision & Electronic Sensors Directorate (United States)


Published in SPIE Proceedings Vol. 8058:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
Harold Szu, Editor(s)

© SPIE. Terms of Use
Back to Top