Due to an increase in the availability of cheap off-the-shelf radio hardware, spoofing and replay attacks on satellite ground systems have become more accessible than ever. This is particularly a problem for legacy systems, many of which do not offer cryptographic security and cannot be patched to support novel security measures. In this paper we explore radio transmitter fingerprinting in satellite systems. We introduce the SatIQ system, proposing novel techniques for authenticating transmissions using characteristics of transmitter hardware expressed as impairments on the downlinked signal. We look in particular at high sample rate fingerprinting, making fingerprints difficult to forge without similarly high sample rate transmitting hardware, thus raising the budget for attacks. We also examine the difficulty of this approach with high levels of atmospheric noise and multipath scattering, and analyze potential solutions to this problem. We focus on the Iridium satellite constellation, for which we collected 1705202 messages at a sample rate of 25 MS/s. We use this data to train a fingerprinting model consisting of an autoencoder combined with a Siamese neural network, enabling the model to learn an efficient encoding of message headers that preserves identifying information. We demonstrate the system's robustness under attack by replaying messages using a Software-Defined Radio, achieving an Equal Error Rate of 0.120, and ROC AUC of 0.946. Finally, we analyze its stability over time by introducing a time gap between training and testing data, and its extensibility by introducing new transmitters which have not been seen before. We conclude that our techniques are useful for building systems that are stable over time, can be used immediately with new transmitters without retraining, and provide robustness against spoofing and replay by raising the required budget for attacks.

翻译：由于廉价商用无线电硬件的普及，针对卫星地面系统的欺骗与重放攻击已变得前所未有地易于实施。这一问题对传统系统尤为严峻——许多老旧系统既不具备密码学安全机制，也无法通过补丁升级支持新型安全措施。本文探索了卫星系统中的无线电发射器指纹识别技术。我们提出SatIQ系统，通过利用下传信号中表征发射器硬件特性的损伤特征，开发了传输认证的新颖技术。我们特别关注高采样率指纹识别方法，使攻击者难以使用同等采样率的发射硬件伪造指纹，从而提升攻击门槛。同时，我们考察了高层大气噪声和多径散射效应下该方法的实施难度，并分析了潜在的解决方案。本研究聚焦铱星卫星星座，以25 MS/s采样率采集了1,705,202条消息。基于此数据，我们训练了由自编码器与孪生神经网络组合构成的指纹识别模型，使模型能够学习保留身份信息的消息头部高效编码。通过使用软件定义无线电重放消息进行攻击测试，我们验证了系统的鲁棒性：等错误率为0.120，受试者工作特征曲线下面积（ROC AUC）达到0.946。最后，我们通过引入训练集与测试集的时间间隔评估其时间稳定性，并通过引入未见过的全新发射器验证其可扩展性。结论表明，本技术有助于构建具备时间稳定性的系统，可在无需重新训练的情况下直接应用于新发射器，并通过提高攻击预算有效抵御欺骗与重放攻击。