Core Concepts
Mouse movement dynamics can serve as a reliable indicator for continuous user authentication, enhancing computer security with machine learning models.
Abstract
The study explores the potential of mouse movement dynamics for continuous authentication in gaming scenarios. Various machine learning models are evaluated across different intensities of user interaction, showcasing their effectiveness in predicting and authenticating users. The research contributes to enhancing computer security by leveraging user behavior, specifically mouse dynamics, for robust authentication systems.
The study collected data from diverse participants playing two contrasting games, Poly Bridge and Team Fortress 2 (TF2), to analyze mouse movement patterns. Results indicate that GRU and LSTM models maintain high performance across training and testing phases, while Decision Tree and Random Forest models show variations in performance metrics. The research highlights the importance of continuous authentication methods in cybersecurity and the significance of incorporating mouse dynamics into authentication frameworks.
Key findings include the consistency of individual mouse dynamics as a reliable metric for user authentication, extending across low and high-intensity gaming interactions. The study's approach spans various gaming environments to provide a comprehensive analysis beyond traditional single-environment studies. Overall, the research validates the efficacy of mouse movement behavior for continuous user authentication and demonstrates competitive performance compared to existing literature.
Stats
Achieved an AUC of 0.99 on training data for GRU model in TF2.
RF model showed an AUC drop to 0.90 on test data in TF2.
LSTM model maintained an average AUC of 0.98 across both training and testing phases in Poly Bridge.
DT model exhibited overfitting issues with reduced test performance in Poly Bridge.
GRU model demonstrated consistent high performance with an average AUC of 0.98 across all datasets.
Quotes
"Mouse movement dynamics can serve as a reliable indicator for continuous user authentication."
"Our findings reveal that mouse movement dynamics can serve as a reliable indicator for continuous user authentication."
"The study's approach spans various gaming environments to provide a comprehensive analysis beyond traditional single-environment studies."