A novel non-linear transformation based multi-user identification algorithm for fixed text keystroke behavioral dynamics

TL;DR

This paper introduces a new non-linear transformation algorithm using keystroke dynamics and localization techniques to accurately identify multiple users sharing a single computer, enhancing security and user verification.

Contribution

The paper presents a novel multi-user identification algorithm based on non-linear transformations and ordinal localization, improving accuracy over existing methods.

Findings

Outperforms existing keystroke-based identification methods

Validated on benchmark datasets with superior accuracy

Effective in shared computer access scenarios

Abstract

In this paper, we propose a new technique to uniquely classify and identify multiple users accessing a single application using keystroke dynamics. This problem is usually encountered when multiple users have legitimate access to shared computers and accounts, where, at times, one user can inadvertently be logged in on another user's account. Since the login processes are usually bypassed at this stage, we rely on keystroke dynamics in order to tell users apart. Our algorithm uses the quantile transform and techniques from localization to classify and identify users. Specifically, we use an algorithm known as ordinal Unfolding based Localization (UNLOC), which uses only ordinal data obtained from comparing distance proxies, by "locating" users in a reduced PCA/Kernel-PCA/t-SNE space based on their typing patterns. Our results are validated with the help of benchmark keystroke datasets and show that our algorithm outperforms other methods.