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Secure Indoor Localization Against Adversarial Attacks Using DCGAN

delete2025-01-01
delete0
PRE
AI
Q
Qingli Yan *
W
Wang Xiong
H
Hui‐Ming Wang
DOI：10.1109/LCOMM.2024.3503721delete
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Abstract

Abstract

En 中文
The vulnerability of deep learning-based indoor Wi-Fi fingerprint localization methods to adversarial attacks significantly reduces localization performance. To overcome this challenge, we propose a defense strategy employing a deep convolutional generative adversarial network (DCGAN) to enhance the security of channel state information (CSI)-based localization methods while maintaining accuracy. Our approach eliminates adversarial perturbations before the adversarial samples are fed into the deep learning model for localization. The localization performance of the proposed DCGAN is evaluated through experiments conducted with commodity Wi-Fi devices in representative indoor environments. Experimental results demonstrate that the DCGAN model effectively mitigates adversarial interference while maintaining excellent localization accuracy under two white-box attacks and one black-box attack.
Keywords:
Location awareness
Tensors
Generators
Training
Perturbation methods
Accuracy
Fingerprint recognition
Wireless fidelity
Security
Receiving antennas
Indoor localization
adversarial attack
CSI
DCGAN

Journal

IEEE Communications Letters cover
IEEE Communications Letters
IF:
4.4
Papers:
1.3W
Citations:
2.2W

Organization

No organization information available