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arXiv · 2110.11080

Continuous Authentication Using Mouse Movements, Machine Learning, and Minecraft

Nyle Siddiqui·Rushit Dave·Naeem Seliya
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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 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.

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Nyle Siddiqui, Rushit Dave, Naeem Seliya. 2021-10-15. Continuous Authentication Using Mouse Movements, Machine Learning, and Minecraft. https://arxiv.org/abs/2110.11080

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