iCTGAN--An Attack Mitigation Technique for Random-vector Attack on Accelerometer-based Gait Authentication Systems

TL;DR

This paper evaluates the vulnerability of accelerometer-based gait authentication systems to random-vector attacks and introduces iABGait, a new method using GANs that improves attack resilience over existing solutions.

Contribution

It proposes iABGait, a novel GAN-based implementation that enhances mitigation of random-vector attacks on gait authentication systems.

Findings

iABGait reduces the impact of random-vector attacks effectively.

iABGait outperforms beta noise-assisted methods in most tests.

The study confirms the vulnerability of vanilla systems to such attacks.

Abstract

A recent study showed that commonly (vanilla) studied implementations of accelerometer-based gait authentication systems ($v$ABGait) are susceptible to random-vector attack. The same study proposed a beta noise-assisted implementation ($\beta$ABGait) to mitigate the attack. In this paper, we assess the effectiveness of the random-vector attack on both $v$ABGait and $\beta$ABGait using three accelerometer-based gait datasets. In addition, we propose $i$ABGait, an alternative implementation of ABGait, which uses a Conditional Tabular Generative Adversarial Network. Then we evaluate $i$ABGait's resilience against the traditional zero-effort and random-vector attacks. The results show that $i$ABGait mitigates the impact of the random-vector attack to a reasonable extent and outperforms $\beta$ABGait in most experimental settings.