Due to the increasing threat of attacks on satellite systems, novel countermeasures have been developed to provide additional security. Among these, there has been a particular interest in transmitter fingerprinting, which authenticates transmitters by looking at characteristics expressed in the physical layer signal. These systems rely heavily upon statistical methods and machine learning, and are therefore vulnerable to a range of attacks. The severity of this threat in a fingerprinting context is currently not well understood. In this paper we evaluate a range of attacks against satellite fingerprinting, building on previous works by looking at attacks optimized to target the fingerprinting system for maximal impact. We design optimized jamming, dataset poisoning, and spoofing attacks, evaluating them in the real world against the SatIQ fingerprinting system designed to authenticate Iridium transmitters, and using a wireless channel emulator to achieve realistic channel conditions. We show that an optimized jamming signal can cause a 50% error rate with attacker-to-victim ratios as low as -30dB (far less power than traditional jamming techniques), and demonstrate successful spoofing attacks, with an attacker successfully removing their own transmitter's fingerprint from messages. We also present a viable dataset poisoning attack, enabling persistent message spoofing by altering stored data to include the fingerprint of the attacker's transmitter. Finally, we show that a model trained to optimize spoofing attacks can also be used to detect spoofing and replay attacks, even when it has never seen the attacker's transmitter before. This technique works even when the training dataset includes only a single transmitter, enabling fingerprinting to be used to protect small constellations and even individual satellites, providing additional protection where it is needed the most.

翻译：由于卫星系统面临的攻击威胁日益加剧，新型防御措施被开发以提供额外安全保障。其中，发射机指纹识别技术因其通过物理层信号中表现出的特征进行发射机认证而受到特别关注。此类系统高度依赖统计方法与机器学习，因此易受多种攻击。目前，指纹识别场景中此类威胁的严重性尚未得到充分认知。本文基于前人研究，通过考察针对指纹识别系统进行优化以实现最大破坏效果的攻击方式，系统评估了针对卫星指纹识别的多种攻击。我们设计了优化的干扰攻击、数据集投毒攻击与欺骗攻击，并在真实环境中使用为认证铱星发射机而设计的SatIQ指纹识别系统进行评估，同时采用无线信道模拟器以复现真实信道条件。实验表明：优化后的干扰信号在攻击者与受害者功率比低至-30dB（远低于传统干扰技术所需功率）时即可引发50%的误判率；同时成功演示了欺骗攻击，攻击者能够从消息中完全消除自身发射机的指纹特征。我们还提出了一种可行的数据集投毒攻击，通过修改存储数据以植入攻击者发射机指纹，实现持久性消息欺骗。最后，我们证明为优化欺骗攻击而训练的模型同样可用于检测欺骗与重放攻击，即使该模型从未接触过攻击者发射机。该技术在训练数据仅包含单个发射机时依然有效，使得指纹识别技术能够用于保护小型卫星星座乃至单颗卫星，在最需要保护的场景中提供额外安全保障。