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

Authors

Jun Hyung Mo, Haverford College
Rajesh Kumar, Bucknell UniversityFollow

Publication Date

10-2022

Conference/Sponsorship/Institution

n IEEE International Joint Conference on Biometrics

Description

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

Type

Conference Paper

Department

Computer Science

Publisher Statement

© 2022 IEEE

Link to published version

https://ieeexplore.ieee.org/document/10007930

Recommended Citation

Mo, Jun Hyung and Kumar, Rajesh, "iCTGAN–An Attack Mitigation Technique for Random-vector Attack on Accelerometer-based Gait Authentication Systems" (2022). Faculty Conference Papers and Presentations. 78.
https://digitalcommons.bucknell.edu/fac_conf/78