User Re-Authentication via Mouse Movements and Recurrent Neural Networks

Paul Houssel, Luis Leiva

2024

Abstract

Behavioral biometrics can determine whether a user interaction has been performed by a legitimate user or an impersonator. In this regard, user re-authentication based on mouse movements has emerged as a reliable and accessible solution, without being intrusive or requiring any explicit input from the user other than regular interactions. Previous work has reported remarkably good classification performance when predicting impersonated mouse movements, however, it has relied on manual data preprocessing or ad-hoc feature extraction methods. In this paper, we design and contrast different recurrent neural networks that take as input raw mouse movements, represented by discrete sequences of coordinate derivatives (coordinate offsets relative to time), as a mean of user re-authentication that could be used on web platforms. We show that a 2-layer BiGRU model outperforms state-of-the-art approaches while being much simpler and more efficient. Our software and models are publicly available.

Paper Citation

in Harvard Style

Houssel P. and Leiva L. (2024). User Re-Authentication via Mouse Movements and Recurrent Neural Networks. In Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-683-5, SciTePress, pages 652-659. DOI: 10.5220/0012296600003648

in Bibtex Style

@conference{icissp24,
author={Paul Houssel and Luis Leiva},
title={User Re-Authentication via Mouse Movements and Recurrent Neural Networks},
booktitle={Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2024},
pages={652-659},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012296600003648},
isbn={978-989-758-683-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - User Re-Authentication via Mouse Movements and Recurrent Neural Networks
SN - 978-989-758-683-5
AU - Houssel P.
AU - Leiva L.
PY - 2024
SP - 652
EP - 659
DO - 10.5220/0012296600003648
PB - SciTePress