This paper presents the development of a keystroke dynamics-based user authentication system using the ARTMAP-FD neural network. The effectiveness of ARTMAP-FD in classifying keystroke patterns is analyzed and compared against a number of widely used machine learning systems. The results show that ARTMAP-FD performs well against many of its counterparts in keystroke patterns classification. Apart from that, instead of using the conventional typing timing characteristics, the applicability of typing pressure to ascertaining user's identity is investigated. The experimental results show that combining both latency and pressure patterns can improve the Equal Error Rate (ERR) of the system.

No abstract

No abstract

No abstract

No abstract