Continuous User Authentication Using Mouse Dynamics, Machine Learning, and Minecraft
Nyle Siddiqui, Rushit Dave, Naeem Seliya
Abstract
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 real-world 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 user's movements and an imposter's 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.