Publication Date

Spring 2021

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Mark Stamp

Second Advisor

Navrati Saxena

Third Advisor

Fabio Di Troia

Keywords

keystroke dynamics, biometrics, CNNs, GRUs

Abstract

The development of active and passive biometric authentication and identification technology plays an increasingly important role in cybersecurity. Biometrics that utilize features derived from keystroke dynamics have been studied in this context. Keystroke dynamics can be used to analyze the way that a user types by monitoring various keyboard inputs. Previous work has considered the feasibility of user authentication and classification based on keystroke features. In this research, we analyze a wide variety of machine learning and deep learning models based on keystroke-derived features, we optimize the resulting models, and we compare our results to those obtained in related research. We find that a model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU) preforms best in our experiments. This model also outperforms previous research in this field.

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