Presenter(s)

Bradley Budach

Abstract

This study explores the use of keystroke dynamics as a behavioral biometric for user identification. Unlike physiological biometrics, such as fingerprints or facial recognition, keystroke dynamics leverages the unique typing patterns of individuals to create a distinctive signature. This research was to develop a machine learning-based system that utilizes keystroke dynamics for continuous and unobtrusive user authentication. By collecting and analyzing keystroke data from multiple users, relevant features were extracted and used to train a machine learning model to identify user keystroke signatures with an equal error rate of 0.11. This model allows for reliable and scalable authentication that can provide an additional layer of security on top of traditional security measures. This study was done as part of my Computer Science Research Seminar course

College

College of Science & Engineering

Department

Computer Science

Campus

Winona

First Advisor/Mentor

Mingrui Zhang

Second Advisor/Mentor

Sudharsan Iyengar

Third Advisor/Mentor

Trung Nguyen

Start Date

4-24-2025 10:00 AM

End Date

4-24-2025 11:00 AM

Presentation Type

Poster Session

Format of Presentation or Performance

In-Person

Session

1b=10am-11am

Poster Number

10

Comments

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Apr 24th, 10:00 AM Apr 24th, 11:00 AM

Using Keystroke Dynamics Behavioral Biometrics to Identify Users

This study explores the use of keystroke dynamics as a behavioral biometric for user identification. Unlike physiological biometrics, such as fingerprints or facial recognition, keystroke dynamics leverages the unique typing patterns of individuals to create a distinctive signature. This research was to develop a machine learning-based system that utilizes keystroke dynamics for continuous and unobtrusive user authentication. By collecting and analyzing keystroke data from multiple users, relevant features were extracted and used to train a machine learning model to identify user keystroke signatures with an equal error rate of 0.11. This model allows for reliable and scalable authentication that can provide an additional layer of security on top of traditional security measures. This study was done as part of my Computer Science Research Seminar course

 

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