Authors: Christopher S. Leberknight; Michael L. Recce
Addresses: Department of Computer Science, Montclair State University, Montclair, NJ, USA ' Recce Analytics, 33 Hillside Avenue, Short Hills, NJ, USA
Abstract: Popular biometric security technologies include fingerprint and iris recognition systems. These technologies are extremely accurate because the patterns associated with an individual's finger or eye are very unique and static. However, when these technologies are used for physical access control they inform the potential adversary that specific characteristics are required to gain access. Behaviometrics aims to develop new strategies to enhance physical security via covert monitoring of distinct behavioral patterns. This research presents a novel stand-alone behaviometric prototype that incorporates standard password security with unique pressure characteristics to covertly analyse individual typing patterns. The prototype is evaluated under a controlled setting with 62 human subjects and nine classification algorithms. The kNN algorithm produced the highest classification rate of 94%. This research is one of the few papers that empirically substantiates biometric performance with a large-scale human subject trial, and also identifies several critical design considerations that impact classifier performance.
Keywords: biometrics; keystroke analysis; pattern recognition; physical security; typing dynamics; embedded systems; keystroke patterns; pressure sensors; behaviometrics; password security; individual typing patterns; kNN; k-nearest neighbour; classifier performance; classification accuracy.
International Journal of Biometrics, 2015 Vol.7 No.3, pp.249 - 270
Received: 05 Oct 2014
Accepted: 30 Jun 2015
Published online: 24 Sep 2015 *