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

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.

AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems

“…Deep learning, an integral part of AI in biometrics, has shown systematic excellence in enhancing authentication processes. However, challenges persist in multibiometrics technology due to nonuniform evaluation standards and user burden, necessitating ongoing optimization and development [33].…”

“…Evidently, the future of biometric authentication lies in the harmonious integration of advanced computational resources with AI algorithms to ensure a system that is not only secure and reliable but also user-friendly and adaptable to evolving technological landscapes [36,33]. The evolving nature of security breaches in cloudbased biometric systems significantly impacts the reliability of AI-based biometric authentication methods, particularly in diverse computational environments and technologies employed in cloud computing.…”