Standard
User Authentication via Multifaceted Mouse Movements and Outlier Exposure. /
Matthiesen, Jennifer J.; Hastedt, Hanne
; Brefeld, Ulf.
Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Hrsg. / Bruno Crémilleux; Sibylle Hess; Siegfried Nijssen. Cham: Springer Nature Switzerland AG, 2023. S. 300-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13876 LNCS).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung
Harvard
Matthiesen, JJ, Hastedt, H
& Brefeld, U 2023,
User Authentication via Multifaceted Mouse Movements and Outlier Exposure. in B Crémilleux, S Hess & S Nijssen (Hrsg.),
Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 13876 LNCS, Springer Nature Switzerland AG, Cham, S. 300-313, 21st International Symposium on Intelligent Data Analysis - IDA 2023, Louvain-la-Neuve, Belgien,
12.04.23.
https://doi.org/10.1007/978-3-031-30047-9_24
APA
Matthiesen, J. J., Hastedt, H.
, & Brefeld, U. (2023).
User Authentication via Multifaceted Mouse Movements and Outlier Exposure. In B. Crémilleux, S. Hess, & S. Nijssen (Hrsg.),
Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings (S. 300-313). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13876 LNCS). Springer Nature Switzerland AG.
https://doi.org/10.1007/978-3-031-30047-9_24
Vancouver
Matthiesen JJ, Hastedt H
, Brefeld U.
User Authentication via Multifaceted Mouse Movements and Outlier Exposure. in Crémilleux B, Hess S, Nijssen S, Hrsg., Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Cham: Springer Nature Switzerland AG. 2023. S. 300-313. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-30047-9_24
Bibtex
@inbook{243d30f8fb314be09cd91ebbf26209b7,
title = "User Authentication via Multifaceted Mouse Movements and Outlier Exposure",
abstract = "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. {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
keywords = "Anomaly Detection, Mouse Dynamics, User Authentication, Authentication, Behavioral research, Mammals, Statistics, Supervised learning, Anomaly detection, Behavioural Biometric, Classification tasks, Mouse dynamics, Mouse movements, Non-intrusive, One-class Classification, State-of-the-art approach, Supervised classification, User authentication, Informatics",
author = "Matthiesen, {Jennifer J.} and Hanne Hastedt and Ulf Brefeld",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 21st International Symposium on Intelligent Data Analysis - IDA 2023, IDA 2023 ; Conference date: 12-04-2023 Through 14-04-2023",
year = "2023",
month = apr,
day = "1",
doi = "10.1007/978-3-031-30047-9_24",
language = "English",
isbn = "978-3-031-30046-2",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature Switzerland AG",
pages = "300--313",
editor = "Bruno Cr{\'e}milleux and Sibylle Hess and Siegfried Nijssen",
booktitle = "Advances in Intelligent Data Analysis XXI",
address = "Switzerland",
url = "https://ida2023.org/",
}
RIS
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 -
