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AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms

Fereidooni, Hossein ; König, Jan ; Rieger, Phillip ; Chilese, Marco ; Goekbakan, Bora ; Finke, Moritz ; Dmitrienko, Alexandra ; Sadeghi, Ahmad-Reza (2023)
AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms.
Network and Distributed Systems Security (NDSS) Symposium 2023. San Diego, USA (27.02.-03.03.2023)
doi: 10.14722/ndss.2023.23194
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Mobile applications are widely used for online services sharing a large amount of personal data online. One-time authentication techniques such as passwords and physiological biometrics (e.g., fingerprint, face, and iris) have their own advantages but also disadvantages since they can be stolen or emulated, and do not prevent access to the underlying device, once it is unlocked. To address these challenges, complementary authentication systems based on behavioural biometrics have emerged. The goal is to continuously profile users based on their interaction with the mobile device. However, existing behavioural authentication schemes are not (i) user-agnostic meaning that they cannot dynamically handle changes in the user-base without model re-training, or (ii) do not scale well to authenticate millions of users.

In this paper, we present AuthentiSense, a user-agnostic, scalable, and efficient behavioural biometrics authentication system that enables continuous authentication and utilizes only motion patterns (i.e., accelerometer, gyroscope, and magnetometer data) while users interact with mobile apps. Our approach requires neither manually engineered features nor a significant amount of data for model training. We leverage a few-shot learning technique, called Siamese network, to authenticate users at a large scale. We perform a systematic measurement study and report the impact of the parameters such as interaction time needed for authentication and n-shot verification (comparison with enrollment samples) at the recognition stage. Remarkably, AuthentiSense achieves high accuracy of up to 97% in terms of F1-score even when evaluated in a few-shot fashion that requires only a few behaviour samples per user (3 shots). Our approach accurately authenticates users only after 1 second of user interaction. For AuthentiSense, we report a FAR and FRR of 0.023 and 0.057, respectively.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2023
Autor(en): Fereidooni, Hossein ; König, Jan ; Rieger, Phillip ; Chilese, Marco ; Goekbakan, Bora ; Finke, Moritz ; Dmitrienko, Alexandra ; Sadeghi, Ahmad-Reza
Art des Eintrags: Bibliographie
Titel: AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms
Sprache: Deutsch
Publikationsjahr: Februar 2023
Veranstaltungstitel: Network and Distributed Systems Security (NDSS) Symposium 2023
Veranstaltungsort: San Diego, USA
Veranstaltungsdatum: 27.02.-03.03.2023
DOI: 10.14722/ndss.2023.23194
URL / URN: https://www.ndss-symposium.org/ndss-paper/authentisense-a-sc...
Kurzbeschreibung (Abstract):

Mobile applications are widely used for online services sharing a large amount of personal data online. One-time authentication techniques such as passwords and physiological biometrics (e.g., fingerprint, face, and iris) have their own advantages but also disadvantages since they can be stolen or emulated, and do not prevent access to the underlying device, once it is unlocked. To address these challenges, complementary authentication systems based on behavioural biometrics have emerged. The goal is to continuously profile users based on their interaction with the mobile device. However, existing behavioural authentication schemes are not (i) user-agnostic meaning that they cannot dynamically handle changes in the user-base without model re-training, or (ii) do not scale well to authenticate millions of users.

In this paper, we present AuthentiSense, a user-agnostic, scalable, and efficient behavioural biometrics authentication system that enables continuous authentication and utilizes only motion patterns (i.e., accelerometer, gyroscope, and magnetometer data) while users interact with mobile apps. Our approach requires neither manually engineered features nor a significant amount of data for model training. We leverage a few-shot learning technique, called Siamese network, to authenticate users at a large scale. We perform a systematic measurement study and report the impact of the parameters such as interaction time needed for authentication and n-shot verification (comparison with enrollment samples) at the recognition stage. Remarkably, AuthentiSense achieves high accuracy of up to 97% in terms of F1-score even when evaluated in a few-shot fashion that requires only a few behaviour samples per user (3 shots). Our approach accurately authenticates users only after 1 second of user interaction. For AuthentiSense, we report a FAR and FRR of 0.023 and 0.057, respectively.

Freie Schlagworte: Behavioural Biometrics, Authentication, Few-shot Learning, and Siamese Networks
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Systemsicherheit
Profilbereiche
Profilbereiche > Cybersicherheit (CYSEC)
Hinterlegungsdatum: 06 Mär 2023 11:57
Letzte Änderung: 06 Jul 2023 08:48
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