Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks

TL;DR

This paper introduces a GAN-assisted touch-based continuous authentication system that significantly improves resistance to population attacks, reducing false accept rates compared to traditional methods.

Contribution

The paper presents a novel G-TCAS framework utilizing GANs to enhance the robustness of touch-based authentication against active adversaries.

Findings

G-TCAS shows lower false accept rate increases than vanilla systems.

G-TCAS achieves a 13-6% FAR increase on average, outperforming vanilla systems.

The framework is validated on a dataset of 117 users interacting with smartphones and tablets.

Abstract

Previous studies have demonstrated that commonly studied (vanilla) touch-based continuous authentication systems (V-TCAS) are susceptible to population attack. This paper proposes a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework, which showed more resilience to the population attack. G-TCAS framework was tested on a dataset of 117 users who interacted with a smartphone and tablet pair. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (22%) than G-TCAS (13%) for the smartphone. Likewise, the increase in the FARs for V-TCAS was 25% compared to G-TCAS (6%) for the tablet.