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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
Conference or Workshop Item, Bibliographie

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.

Item Type: Conference or Workshop Item
Erschienen: 2023
Creators: Fereidooni, Hossein ; König, Jan ; Rieger, Phillip ; Chilese, Marco ; Goekbakan, Bora ; Finke, Moritz ; Dmitrienko, Alexandra ; Sadeghi, Ahmad-Reza
Type of entry: Bibliographie
Title: AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms
Language: German
Date: February 2023
Event Title: Network and Distributed Systems Security (NDSS) Symposium 2023
Event Location: San Diego, USA
Event Dates: 27.02.-03.03.2023
DOI: 10.14722/ndss.2023.23194
URL / URN: https://www.ndss-symposium.org/ndss-paper/authentisense-a-sc...
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.

Uncontrolled Keywords: Behavioural Biometrics, Authentication, Few-shot Learning, and Siamese Networks
Divisions: 20 Department of Computer Science
20 Department of Computer Science > System Security Lab
Profile Areas
Profile Areas > Cybersecurity (CYSEC)
Date Deposited: 06 Mar 2023 11:57
Last Modified: 06 Jul 2023 08:48
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