Security Analysis of WiFi-Based Sensing Systems: Threats From Perturbation Attacks

Summary

Researchers present WiIntruder, a perturbation attack against deep learning models used in WiFi-based wireless sensing. The method aims for universality, robustness and stealthiness by improving transferability across sensing models, optimizing device synchronization and propagation factors with a particle swarm algorithm, and varying attack patterns from a generative adversarial network. In black-box scenarios, average accuracy across four common WiFi-based services dropped by 72.9%.

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