IriSig-Spoof: A Real-World Benchmark for Time-Robust Satellite RF Fingerprinting and Spoofing Detection

• URL: http://arxiv.org/abs/2608.18642v1
• Date: Wed, 19 Aug 2026 07:43:32 GMT
• Title: IriSig-Spoof: A Real-World Benchmark for Time-Robust Satellite RF Fingerprinting and Spoofing Detection
• Abstract summary: Radio frequency fingerprinting (RFF) offers a potential defense against satellite impersonation and signal spoofing.<n>No unified dataset and benchmark support temporal, open-set, and cross-scenario evaluation.<n>IriSig-Spoof is a real-world Iridium dataset comprising 5.17 million messages collected from 66 satellites over 32 days.
• Score: 19.16082240921973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low Earth orbit (LEO) satellite Internet is becoming critical communications infrastructure, yet its open wireless links remain vulnerable to satellite impersonation and signal spoofing. Radio frequency fingerprinting (RFF) offers a potential defense by exploiting transmitter-specific hardware imperfections manifested in received signals. However, the reliability of existing satellite RFF methods remains difficult to assess because no unified dataset and benchmark support temporal, open-set, and cross-scenario evaluation. To address this gap, we introduce IriSig-Spoof, a real-world Iridium dataset comprising 5.17 million messages collected from 66 satellites over 32 days, together with software-defined radio (SDR)-generated spoofing signals from indoor and outdoor settings. We further establish three benchmark tasks: temporal robustness evaluation, open-set RFF identification with unknown-signal rejection, and cross-scenario spoofing detection. Experiments using a multi-scale attention convolutional neural network (MACNN) show that temporal robustness varies across configurations, with the best configuration achieving 97.75% average cross-day accuracy. In open-set evaluation, MACNN achieves an area under the receiver operating characteristic curve (AUROC) of 0.9715, while showing that effective unknown-signal rejection does not necessarily ensure reliable identity assignment. Cross-scenario experiments reveal differences at low false-positive rates. IriSig-Spoof provides a reproducible basis for evaluating robust RFF methods under temporal variation and changing attack conditions.

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