TY - CHAP

T1 - User Authentication via Multifaceted Mouse Movements and Outlier Exposure

AU - Matthiesen, Jennifer J.

AU - Hastedt, Hanne

AU - Brefeld, Ulf

N1 - Conference code: 21

PY - 2023/4/1

Y1 - 2023/4/1

N2 - Gaining information about how users interact with systems is key to behavioural biometrics. Particularly mouse movements of users have been proven beneficial to authentication tasks for being inexpensive and non-intrusive. State-of-the-art approaches consider this problem an instance of supervised classification tasks. In this paper, we argue that the problem is actually closer to unsupervised one-class classification tasks. We thus propose to view behavioural user authentication as an unsupervised task and learn individual models using data from a single user only. We further show that, by being purely unsupervised, losses in performance can be counterbalanced by augmenting additional data into the training processes (outlier exposure). Empirical results show that our approach is very effective and outperforms the state-of-the-art in several performance metrics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

AB - Gaining information about how users interact with systems is key to behavioural biometrics. Particularly mouse movements of users have been proven beneficial to authentication tasks for being inexpensive and non-intrusive. State-of-the-art approaches consider this problem an instance of supervised classification tasks. In this paper, we argue that the problem is actually closer to unsupervised one-class classification tasks. We thus propose to view behavioural user authentication as an unsupervised task and learn individual models using data from a single user only. We further show that, by being purely unsupervised, losses in performance can be counterbalanced by augmenting additional data into the training processes (outlier exposure). Empirical results show that our approach is very effective and outperforms the state-of-the-art in several performance metrics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

KW - Anomaly Detection

KW - Mouse Dynamics

KW - User Authentication

KW - Authentication

KW - Behavioral research

KW - Mammals

KW - Statistics

KW - Supervised learning

KW - Anomaly detection

KW - Behavioural Biometric

KW - Classification tasks

KW - Mouse dynamics

KW - Mouse movements

KW - Non-intrusive

KW - One-class Classification

KW - State-of-the-art approach

KW - Supervised classification

KW - User authentication

KW - Informatics

UR - http://www.scopus.com/inward/record.url?scp=85152590477&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/5ddac6d1-9710-363c-8202-b625f1b82c3f/

U2 - 10.1007/978-3-031-30047-9_24

DO - 10.1007/978-3-031-30047-9_24

M3 - Article in conference proceedings

SN - 978-3-031-30046-2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 300

EP - 313

BT - Advances in Intelligent Data Analysis XXI

A2 - Crémilleux, Bruno

A2 - Hess, Sibylle

A2 - Nijssen, Siegfried

PB - Springer Nature Switzerland AG

CY - Cham

T2 - 21st International Symposium on Intelligent Data Analysis - IDA 2023

Y2 - 12 April 2023 through 14 April 2023

ER -