Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Contextual Word Embeddings Clustering Through Multiway Analysis: A Comparative Study Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

User Authentication via Multifaceted Mouse Movements and Outlier Exposure

verfasst von : Jennifer J. Matthiesen, Hanne Hastedt, Ulf Brefeld

Erschienen in: Advances in Intelligent Data Analysis XXI

Verlag: Springer Nature Switzerland

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel APs: A Proxemic Framework for Social Media Interactions Modeling and Analysis
Nächstes Kapitel Explaining Black Box Reinforcement Learning Agents Through Counterfactual Policies
Literatur
1.
Antal, M., Fejér, N.: Mouse dynamics based user recognition using deep learning. Acta Universitatis Sapientiae, Informatica 12, 39–50 (2020)CrossRef
2.
Arapakis, I., Lalmas, M., Valkanas, G.: Understanding within-content engagement through pattern analysis of mouse gestures. In: Proc. of the 23rd ACM CIKM, pp. 1439–1448 (2014)
3.
Arapakis, I., Leiva, L.A.: Predicting user engagement with direct displays using mouse cursor information. In: Proc. of the 39th Int. ACM SIGIR Conf. on Research and Development in Inform. Retrieval, pp. 599–608. ACM (2016)
4.
Atterer, R., Wnuk, M., Schmidt, A.: Knowing the user’s every move: user activity tracking for website usability evaluation and implicit interaction. In: Proc. of the 15th Int. Conf. on WWW (2006)
5.
6.
7.
Chong, P., Elovici, Y., Binder, A.: User authentication based on mouse dynamics using deep neural networks: a comprehensive study. In: IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1086–1101 (2020)
8.
Chong, P., Ruff, L., Kloft, M., Binder, A.: Simple and effective prevention of mode collapse in deep one-class classification. CoRR abs/2001.08873 (2020)
9.
10.
11.
Feher, C., Elovici, Y., Moskovitch, R., Rokach, L., Schclar, A.: User identity verification via mouse dynamics. Inf. Sci. 201, 19–36 (2012)CrossRef
12.
13.
14.
Gamboa, H., Fred, A.: A behavioral biometric system based on human-computer interaction. In: Jain, A.K., Ratha, N.K. (eds.) Biometric Technology for Human Identification, vol. 5404, pp. 381–392. SPIE (2004)
15.
Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)
16.
Görnitz, N., Kloft, M., Rieck, K., Brefeld, U.: Toward supervised anomaly detection. J. Artif. Intell. Res. 46, 235–262 (2013)MathSciNetCrossRefMATH
17.
Haider, P., Chiarandini, L., Brefeld, U.: Discriminative clustering for market segmentation. In: Proc. of the ACM SIGKDD (2012)
18.
Harilal, A., Toffalini, F., Castellanos, J., Guarnizo, J., Homoliak, I., Ochoa, M.: Twos: a dataset of malicious insider threat behavior based on a gamified competition. In: Proc. of MIST (MIST 2017), pp. 45–56. Association for Comp. Machinery, New York, NY, USA (2017)
19.
Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: International Conference on Learning Representations (2019)
20.
Hendrycks, D., Mazeika, M., Kadavath, S., Song, D.: Using self-supervised learning can improve model robustness and uncertainty. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)
21.
Kaixin, W., Hongri, L., Bailing, W., Shujie, H., Jia, S.: A user authentication and identification model based on mouse dynamics. In: Proceedings of the 6th Int. Conf. on Information Engineering (ICIE 2017), Association for Computing Machinery, New York, NY, USA (2017)
22.
Komarinski, P.: Automated fingerprint identification systems (AFIS). Elsevier (2005)
23.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc. (2012)
24.
Lagun, D., Ageev, M., Guo, Q., Agichtein, E.: Discovering common motifs in cursor movement data for improving web search. In: Proc. of the 7th ACM Int. Conf. on Web Search and Data Mining, pp. 183–192. ACM (2014)
25.
26.
Mondal, S., Bours, P.: A study on continuous authentication using a combination of keystroke and mouse biometrics. Neurocomputing 230, 1–22 (2017)CrossRef
27.
Perera, P., Patel, V.M.: Deep transfer learning for multiple class novelty detection. CoRR abs/1903.02196 (2019)
28.
Perera, P., Patel, V.M.: Learning deep features for one-class classification. IEEE Trans. Image Process. 28(11), 5450–5463 (2019)MathSciNetCrossRefMATH
29.
Rodden, K., Fu, X., Aula, A., Spiro, I.: Eye-mouse coordination patterns on web search results pages. In: Extended Abstracts on Human Factors in Computing Systems (CHI EA 2008), pp. 2997–3002. Assoc. for Computing Machinery, New York, NY, USA (2008)
30.
Ruff, L., et al.: Deep one-class classification. In: ICML, pp. 4393–4402 (2018)
31.
Ruff, L., Vandermeulen, R.A., Franks, B.J., Müller, K.R., Kloft, M.: Rethinking assumptions in deep anomaly detection (2020)
32.
Ruff, L., et al.: Deep semi-supervised anomaly detection. In: Int. Conf. on Learning Representations (2020)
33.
Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)CrossRefMATH
34.
Shen, C., Cai, Z., Guan, X., Du, Y., Maxion, R.A.: User authentication through mouse dynamics. In: IEEE Trans. Inf. Forensics Security, vol. 8, pp. 16–30 (2013)
35.
Shen, C., Cai, Z., Liu, X., Guan, X., Maxion, R.A.: MouseIdentity: modeling mouse-interaction behavior for a user verification system. IEEE Trans. Hum.-Mach. Syst. 46(5), 734–748 (2016)CrossRef
36.
Shen, C., Cai, Z., Maxion, R.A., Xiang, G., Guan, X.: Comparing classification algorithm for mouse dynamics based user identification. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 61–66 (2012)
37.
Tax, D.M., Duin, R.P.: Support vector data description. ML 54(1), 45–66 (2004)MATH
38.
Tzafilkou, K., Protogeros, N.: Mouse behavioral patterns and keystroke dynamics in end-user development: what can they tell us about users’ behavioral attributes? In: Computers in Human Behavior, vol. 83, pp. 288–305 (2018)
39.
40.
Yao, B., Ai, H., Lao, S.: Person-specific face recognition in unconstrained environments: a combination of offline and online learning. In: 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1–8 (2008)
41.
Zhang, L., Tan, S., Yang, J., Chen, Y.: VoiceLive: a phoneme localization based liveness detection for voice authentication on smartphones. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS 2016), pp. 1080–1091. Association for Computing Machinery, New York, NY, USA (2016)
42.
Zimmermann, P., Guttormsen, S., Danuser, B., Gomez, P.: Affective computing - measuring mood with mouse and keyboard. Int. J. Occup. Saf. Ergon. 9, 539–551 (2003)CrossRef
Metadaten
Titel
User Authentication via Multifaceted Mouse Movements and Outlier Exposure
verfasst von
Jennifer J. Matthiesen
Hanne Hastedt
Ulf Brefeld
Copyright-Jahr
2023
Buch
Advances in Intelligent Data Analysis XXI

Print ISBN: 978-3-031-30046-2

Electronic ISBN: 978-3-031-30047-9

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-30047-9_24

Premium Partner

    Bildnachweise