Towards behaviometric security systems: Learning to identify a typist

Abstract

We consider the problem of identifying a user typing on a computer keyboard based on patterns in the time series consisting of keyboard events. We develop a learning algorithm, which can rather accurately learn to authenticate and protect users. Our solution is based on a simple extension of the well known Lempel-Ziv (78) universal compression algorithm. A novel application of our results is a second-layer behaviometric security system, which continually examines the current user without interfering with this user's work while attempting to identify unauthorized users pretending to be the user. We study the utility of our methods over a real dataset consisting of 5 users and 30 'attackers'.