Papers for

wireless security teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Hardware changes detected using frequency and spatial fingerprinting

FreqSpaNet: Frequency and Spatial Learning of SFPF for Physical Layer Hardware Integrity Detection

Abstract: 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.

Tue 15 SeptMachine Learning

The gist

Unauthorized changes to wireless devices can keep them looking the same but alter their physical parts, which is hard to detect. The authors present a new method that looks at device signals in terms of frequency and direction to spot these changes. Their approach uses separate ways to understand frequency details and spatial directions, then combines them to improve detection. Tests show their method works better than previous ones at finding replaced hardware parts.

Open → 2609.17491v1