Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis

TL;DR

This paper introduces a new web-based, realistic keystroke dynamics dataset and provides a statistical analysis of factors affecting system performance, highlighting the importance of environment and password characteristics.

Contribution

It offers a novel dataset collected in uncontrolled environments and analyzes key factors influencing keystroke biometric system performance.

Findings

Performance improves with larger passwords.

Fusion schemes can enhance accuracy.

Realistic conditions affect keystroke dynamics results.

Abstract

Most keystroke dynamics studies have been evaluated using a specific kind of dataset in which users type an imposed login and password. Moreover, these studies are optimistics since most of them use different acquisition protocols, private datasets, controlled environment, etc. In order to enhance the accuracy of keystroke dynamics' performance, the main contribution of this paper is twofold. First, we provide a new kind of dataset in which users have typed both an imposed and a chosen pairs of logins and passwords. In addition, the keystroke dynamics samples are collected in a web-based uncontrolled environment (OS, keyboards, browser, etc.). Such kind of dataset is important since it provides us more realistic results of keystroke dynamics' performance in comparison to the literature (controlled environment, etc.). Second, we present a statistical analysis of well known assertions such as the relationship between performance and password size, impact of fusion schemes on system overall performance, and others such as the relationship between performance and entropy. We put into obviousness in this paper some new results on keystroke dynamics in realistic conditions.