Keystroke Dynamics for User Identification

TL;DR

This paper explores keystroke dynamics for user identification using complex image-like features and deep learning, achieving high accuracy with Random Forest classifiers on free-text data from 148 users.

Contribution

It introduces a novel application of image-like features and compares deep learning with traditional classifiers for multiclass user identification.

Findings

Random Forest achieves 93% accuracy

Deep learning achieves 78% accuracy

Features improve identification performance

Abstract

In previous research, keystroke dynamics has shown promise for user authentication, based on both fixed-text and free-text data. In this research, we consider the more challenging multiclass user identification problem, based on free-text data. We experiment with a complex image-like feature that has previously been used to achieve state-of-the-art authentication results over free-text data. Using this image-like feature and multiclass Convolutional Neural Networks, we are able to obtain a classification (i.e., identification) accuracy of 0.78 over a set of 148 users. However, we find that a Random Forest classifier trained on a slightly modified version of this same feature yields an accuracy of 0.93.