  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.
