Investigating Continuous Authentication via Mouse Dynamics for Security

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."

Key Insights Distilled From

From Clicks to Security

by Rushit Dave,... at arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03828.pdf

Deeper Inquiries

How can the findings from this study be applied to real-world security systems beyond gaming environments?

The findings from this study on continuous authentication via mouse dynamics have significant implications for real-world security systems. Beyond gaming environments, these insights can be applied to various industries and sectors where user authentication is crucial. For instance, in financial institutions, healthcare facilities, government agencies, and corporate organizations, implementing continuous authentication based on mouse movement patterns can enhance security measures. By leveraging machine learning models like GRU, LSTM, Decision Tree, and Random Forest as demonstrated in the study, organizations can develop robust authentication systems that continuously monitor user behavior for heightened security.

What are some potential limitations or challenges associated with using mouse dynamics for continuous user authentication?

While using mouse dynamics for continuous user authentication offers several advantages such as non-intrusiveness and adaptability to dynamic interactions with a system, there are also potential limitations and challenges to consider. One limitation is the possibility of false positives or false negatives in the authentication process due to variations in individual behavior or external factors affecting mouse movements. Additionally, ensuring data privacy and protection while collecting and analyzing sensitive behavioral biometric data poses a challenge. Moreover, users may exhibit different behaviors over time which could impact the accuracy of the authentication system based on historical data.

How might advancements in machine learning impact the future development of behavioral biometric authentication methods?

Advancements in machine learning are poised to revolutionize the future development of behavioral biometric authentication methods by enhancing accuracy, efficiency, and adaptability. With ongoing research into deep learning techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), behavioral biometric models can become more adept at capturing intricate patterns in user behavior for improved identification accuracy. Furthermore, advancements in model interpretability will enable better understanding of how machine learning algorithms make decisions based on behavioral biometrics data. This transparency will be essential for building trust among users regarding their privacy and security when utilizing such advanced technologies.