AI 中文总结
本文推出真实世界卫星射频指纹与欺骗检测基准IriSig-Spoof，构建三项评估任务，采用多尺度注意力卷积神经网络实验验证了时间鲁棒性等性能，为相关方法评估提供可复现基础。
AI 中文摘要
低地球轨道（LEO）卫星互联网正成为关键通信基础设施，但其开放无线链路仍易受卫星冒充与信号欺骗攻击。射频指纹（RFF）可利用接收信号中体现的发射机特定硬件缺陷提供潜在防御手段。然而，现有卫星RFF方法的可靠性难以评估，因为缺乏统一数据集和基准来支持时间维度、开放集及跨场景评估。为填补这一空白，本文推出IriSig-Spoof，这是一个真实世界铱星（Iridium）数据集，包含从66颗卫星历时32天收集的517万条消息，以及软件定义无线电（SDR）生成的室内外场景欺骗信号。本文进一步确立三项基准任务：时间鲁棒性评估、带未知信号拒绝的开放集RFF识别，以及跨场景欺骗检测。采用多尺度注意力卷积神经网络（MACNN）开展的实验表明，不同配置的时间鲁棒性存在差异，最优配置的跨天平均准确率达97.75%；在开放集评估中，MACNN的受试者工作特征曲线下面积（AUROC）为0.9715，同时显示有效的未知信号拒绝并不一定能确保可靠的身份分配；跨场景实验在低误报率下呈现出差异。IriSig-Spoof为评估时间变化及攻击条件改变下的鲁棒RFF方法提供了可复现的基础。
英文摘要
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.
