# Security Analysis of WiFi-based Sensing Systems: Threats from Perturbation Attacks

arXiv: https://arxiv.org/abs/2404.15587 · HTML: https://mlsanity.com/paper/2404.15587/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2024.

## Built on by (most cited first, 6 of 6)

- [GCP: Guarded Collaborative Perception with Spatial-Temporal Aware Malicious Agent Detection](https://mlsanity.com/paper/2501.02450/) (2025)
- [Secure Traffic Sign Recognition: An Attention-Enabled Universal Image Inpainting Mechanism against Light Patch Attacks](https://mlsanity.com/paper/2409.04133/) (2024)
- [Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness](https://mlsanity.com/paper/2407.04016/) (2024)
- [Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting](https://mlsanity.com/paper/2409.00086/) (2024)
- [ZK-SenseLM: Verifiable Large-Model Wireless Sensing with Selective Abstention and Zero-Knowledge Attestation](https://mlsanity.com/paper/2510.25677/) (2025)
- [Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness](https://mlsanity.com/paper/2511.20456/) (2025)

## Abstract (arXiv)

Deep learning technologies are pivotal in enhancing the performance of WiFi-based wireless sensing systems. However, they are inherently vulnerable to adversarial perturbation attacks, and regrettably, there is lacking serious attention to this security issue within the WiFi sensing community. In this paper, we elaborate such an attack, called WiIntruder, distinguishing itself with universality, robustness, and stealthiness, which serves as a catalyst to assess the security of existing WiFi-based sensing systems. This attack encompasses the following salient features: (1) Maximizing transferability by differentiating user-state-specific feature spaces across sensing models, leading to a universally effective perturbation attack applicable to common applications; (2) Addressing perturbation signal distortion caused by device synchronization and wireless propagation when critical parameters are optimized through a heuristic particle swarm-driven perturbation generation algorithm; and (3) Enhancing attack pattern diversity and stealthiness through random switching of perturbation surrogates generated by a generative adversarial network. Extensive experimental results confirm the practical threats of perturbation attacks to common WiFi-based services, including user authentication and respiratory monitoring.