A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices

TL;DR

This paper introduces a mobile-friendly behavioral biometric authentication framework using deep metric learning, enabling on-device training, enhanced discriminative power, and robustness against attacks with high accuracy and low resource consumption.

Contribution

It presents a novel on-device training framework with deep metric learning for behavioral biometrics, addressing privacy, adaptability, and security challenges in mobile authentication.

Findings

Authentication accuracy exceeds 95% on public datasets.

Training on mobile CPUs is feasible within 10 minutes per 100 epochs.

Robust against brute-force and side-channel attacks with success rates of 99% and 90%.

Abstract

Mobile authentication using behavioral biometrics has been an active area of research. Existing research relies on building machine learning classifiers to recognize an individual's unique patterns. However, these classifiers are not powerful enough to learn the discriminative features. When implemented on the mobile devices, they face new challenges from the behavioral dynamics, data privacy and side-channel leaks. To address these challenges, we present a new framework to incorporate training on battery-powered mobile devices, so private data never leaves the device and training can be flexibly scheduled to adapt the behavioral patterns at runtime. We re-formulate the classification problem into deep metric learning to improve the discriminative power and design an effective countermeasure to thwart side-channel leaks by embedding a noise signature in the sensing signals without sacrificing too much usability. The experiments demonstrate authentication accuracy over 95% on three public datasets, a sheer 15% gain from multi-class classification with less data and robustness against brute-force and side-channel attacks with 99% and 90% success, respectively. We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer. Finally, we profile memory, energy and computational overhead. Our results indicate that training consumes lower energy than watching videos and slightly higher energy than playing games.