SATversary: Adversarial Attacks and Defenses for Satellite Fingerprinting

TL;DR

This paper evaluates sophisticated adversarial attacks on satellite fingerprinting systems, demonstrating vulnerabilities and proposing methods for both attack and detection, thereby enhancing understanding of security in satellite communication authentication.

Contribution

It introduces optimized attack strategies against satellite fingerprinting and proposes a detection method that works even with minimal training data, advancing satellite security research.

Findings

Optimized jamming causes 50% error rate at -30dB attacker-to-victim ratio.

Successful spoofing attacks can remove transmitter fingerprints from messages.

A model trained for spoofing detection can identify attacks even with limited data.

Abstract

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.