Abstract

With the surge in information management technology reliance and the looming presence of cyber threats, user authentication has become paramount in computer security. Traditional static or one-time authentication has its limitations, prompting the emergence of continuous authentication as a frontline approach for enhanced security. Continuous authentication taps into behavior-based metrics for ongoing user identity validation, predominantly utilizing machine learning techniques to continually model user behaviors. This study elucidates the potential of mouse movement dynamics as a key metric for continuous authentication. By examining mouse movement patterns across two contrasting gaming scenarios - the high-intensity "Team Fortress" and the low-intensity strategic "Poly Bridge" the research illuminates the distinct behavioral imprints users leave behind. Such consistent and unique mouse movements emphasize their credibility as reliable biometric markers. The developed sequential model in this research not only demonstrates impressive performance in user verification across these environments but also surpasses benchmarks set by prior research in the field. These findings underscore the potential of mouse movements in revolutionizing the continuous authentication domain, offering heightened security while capturing the intricacies of user behavior across diverse contexts.

Advisor

Rushit Dave

Committee Member

Rajeev Bukralia

Committee Member

Mansi Bhavsar

Date of Degree

2023

Language

english

Document Type

APP

Degree

Master of Science (MS)

Program of Study

Data Science

Department

Computer Information Science

College

Science, Engineering and Technology

Download

Share

COinS
 

Rights Statement

In Copyright