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 1010464 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：随着低成本商用无线电硬件的普及，针对卫星地面系统的欺骗和重放攻击变得前所未有地容易。这一问题对传统系统尤为严峻，其中许多系统既未提供加密安全手段，也无法通过补丁支持新型安全机制。本文探索了卫星系统中的无线电发射机指纹识别技术。我们提出SatIQ系统，通过利用发射机硬件特性（表现为下行信号的损伤）来创新性地认证传输信号。我们特别关注高采样率指纹识别，使指纹难以被不具备同等高采样率的发射硬件伪造，从而提升攻击门槛。同时，我们考察了该方法在高噪声和多径散射环境下的实现难度，并分析了潜在解决方案。研究聚焦于铱星卫星星座，我们以25 MS/s的采样率收集了1,010,464条消息，用于训练由自编码器与孪生神经网络组合而成的指纹识别模型。该模型能学习保留身份信息的消息头高效编码。通过软件定义无线电重放消息进行攻击测试，系统等错误率为0.120，ROC AUC值为0.946，证明了其鲁棒性。最后，我们通过引入训练数据与测试数据的时间间隔分析其时间稳定性，并通过引入未知新发射机分析其可扩展性。结论表明，所提技术有助于构建时间稳定的系统，可无需重新训练即用于新发射机，并通过提高攻击预算有效抵御欺骗和重放攻击。