iCTGAN--An Attack Mitigation Technique for Random-vector Attack on Accelerometer-based Gait Authentication Systems
This addresses security risks in biometric authentication systems for users, but it is incremental as it builds on prior work to improve attack resilience.
The paper tackles the vulnerability of accelerometer-based gait authentication systems to random-vector attacks by proposing iABGait, a new implementation using a Conditional Tabular Generative Adversarial Network, which reduces the attack impact and outperforms a previous mitigation method in most settings.
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 ($β$ABGait) to mitigate the attack. In this paper, we assess the effectiveness of the random-vector attack on both $v$ABGait and $β$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 $β$ABGait in most experimental settings.