Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model
Physical-layer spoofing attacks pose a critical threat to wireless localization systems by manipulating channel state information (CSI). Detecting such attacks becomes particularly challenging when the attacker and the legitimate node are spatially proximate, as their CSI characteristics become increasingly similar. To address this challenge in indoor scenarios, we propose an adaptive Gaussian mixture model (GMM)-based detection framework that learns legitimate CSI patterns and detects spatial inconsistency through statistical anomaly detection. Using a massive multiple-input multiple-output (MIMO) experimental dataset with 64 uniform linear array (ULA) antenna and 5 mm spatial resolution...
| Authors | Mamady Delamou, Luan Chen, Arsenia Chorti |
|---|---|
| Primary category | cs.CR |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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