  Mouse dynamics has grown in popularity as a novel irreproducible behavioral
biometric. Datasets which contain general unrestricted mouse movements from
users are sparse in the current literature. The Balabit mouse dynamics dataset
produced in 2016 was made for a data science competition and despite some of
its shortcomings, is considered to be the first publicly available mouse
dynamics dataset. Collecting mouse movements in a dull administrative manner as
Balabit does may unintentionally homogenize data and is also not representative
of realworld application scenarios. This paper presents a novel mouse dynamics
dataset that has been collected while 10 users play the video game Minecraft on
a desktop computer. Binary Random Forest (RF) classifiers are created for each
user to detect differences between a specific users movements and an imposters
movements. Two evaluation scenarios are proposed to evaluate the performance of
these classifiers; one scenario outperformed previous works in all evaluation
metrics, reaching average accuracy rates of 92%, while the other scenario
successfully reported reduced instances of false authentications of imposters.
