Continuous Authentication Using Mouse Movements, Machine Learning, and Minecraft

TL;DR

This paper introduces a new mouse dynamics dataset collected during Minecraft gameplay and demonstrates its effectiveness for continuous user authentication using Random Forest classifiers, achieving high accuracy and low false positive rates.

Contribution

The study presents a novel dataset of mouse movements during gaming and applies machine learning for authentication, outperforming previous methods in accuracy and false acceptance rates.

Findings

Achieved 92% average accuracy in user authentication.

Proposed evaluation scenarios improve false authentication rates.

Introduced a realistic dataset from gaming context.

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.