FreqSpaNet: Frequency and Spatial Learning of SFPF for Physical Layer Hardware Integrity Detection
FreqSpaNet learns spatio-frequency polarization fingerprints to detect unauthorized wireless hardware replacement, reaching 96.31% mean AUROC across seven replacement scenarios.
FreqSpaNet is a representation learning network for open-set hardware anomaly detection using spatio-frequency polarization fingerprints (SFPFs), which capture device-dependent responses across frequencies and directions. A frequency branch models local variations among neighboring frequencies while a geometry-aware spatial branch models directional relationships via angular information, combined through adaptive fusion and complementary pretraining. It achieves a mean AUROC of 96.31%, 9.05 points above the baseline, and is verified under seven hardware replacement scenarios.
- Detects unauthorized hardware replacement that preserves logical device identity
- Frequency branch plus geometry-aware spatial branch with adaptive fusion
- 96.31% mean AUROC, 9.05 points above baseline
- Validated across seven hardware replacement scenarios
Full article131 words · extracted from arxiv.org · click to collapse
Unauthorized hardware replacement can preserve a wireless device's logical identity while altering its physical implementation, posing a challenge to hardware integrity verification. Spatio-frequency polarization fingerprints (SFPFs) capture device-dependent responses across multiple frequencies and directions, but their frequency and spatial dimensions exhibit different structural dependencies. We propose FreqSpaNet, an SFPF representation learning network for open set hardware anomaly detection. A frequency branch captures local variations among neighboring frequencies, while a geometry-aware spatial branch models directional relationships using angular information. The two representations are combined through adaptive fusion, and complementary pretraining further captures shared information while preserving the distinct characteristics of the frequency and spatial representations. Experiments show that FreqSpaNet achieves a mean AUROC of 96.31\%, 9.05 points above the baseline. Results under seven hardware replacement scenarios further verify the effectiveness of FreqSpaNet.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.17491