Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics

TL;DR

This paper explores machine learning and deep learning methods for user authentication using fixed-text keystroke dynamics, demonstrating that optimized models like XGBoost and MLP outperform previous approaches.

Contribution

It systematically compares various ML and DL techniques on keystroke data, optimizing models and achieving superior performance over prior research.

Findings

XGBoost and MLP models perform well in keystroke-based authentication.

Optimized models outperform previous research results.

Fixed-text keystroke features are effective for user identification.

Abstract

Keystroke dynamics can be used to analyze the way that users type by measuring various aspects of keyboard input. Previous work has demonstrated the feasibility of user authentication and identification utilizing keystroke dynamics. In this research, we consider a wide variety of machine learning and deep learning techniques based on fixed-text keystroke-derived features, we optimize the resulting models, and we compare our results to those obtained in related research. We find that models based on extreme gradient boosting (XGBoost) and multi-layer perceptrons (MLP)perform well in our experiments. Our best models outperform previous comparable research.