Remote suspect identification and the impact of demographic features on keystroke dynamics

Abstract

This paper describes the research, development, and analysis performed during the Remote Suspect Identification (RSID) effort. The effort produced a keystroke dynamics sensor capable of authenticating, continuously verifying, and identifying masquerading users with equal error rates (EER) of approximately 0.054, 0.050, and 0.069, respectively. This sensor employs 11 distinct algorithms, each using between one and five keystroke features, that are fused (across features and algorithms) using a weighted majority ballot algorithm to produce rapid and accurate measurements. The RSID sensor operates discretely, quickly (using few keystrokes), and requires no additional hardware. The researchers also analyzed the difference in sensor performance across 10 demographic features using a keystroke dynamics dataset consisting of data from over 2,200 subjects. This analysis indicated that there are significant and discernible differences across age groups, ethnicities, language, handedness, height, occupation, sex, typing frequency, and typing style.