Publication Date

Spring 2023

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Fabio Di Troia

Second Advisor

Robert Chun

Third Advisor

Pranav Cherukupalli

Keywords

Behavioral Biometrics, Mouse Dynamics, Transformer models, Recurrent Neural Networks

Abstract

Increasing reliance on digital services and the limitations of traditional authentication methods have necessitated the development of more advanced and secure user authentication methods. For user authentication and intrusion detection, mouse dynamics, a form of behavioral biometrics, offers a promising and non-invasive method. This paper presents a comprehensive study on ML-Based User Authentication Through Mouse Dynamics.

This project proposes a novel framework integrating sophisticated techniques such as embeddings extraction using Transformer models with cutting-edge machine learning algorithms such as Recurrent Neural Networks (RNN). The project aims to accurately identify users based on their distinct mouse behavior and detect unauthorized access by utilizing the hybrid models. Using a mouse dynamics dataset, the proposed framework’s performance is evaluated, demonstrating its efficacy in accurately identifying users and detecting intrusions.

In addition, a comparative analysis with existing methodologies is provided, highlighting the enhancements made by the proposed framework. This paper contributes to the development of more secure, reliable, and user-friendly authentication systems that leverage the power of machine learning and behavioral biometrics, ultimately augmenting the privacy and security of digital services and resources.

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