On Detecting Deception in Space Situational Awareness

Space Situational Awareness (SSA) data is critical to the safe piloting of satellites through an ever-growing field of orbital debris. However, measurement complexity means that most satellite operators cannot independently acquire SSA data and must rely on a handful of centralized repositories operated by major space powers. As interstate competition in orbit increases, so does the threat of attacks abusing these information-sharing relationships. This paper offers one of the first considerations of defense techniques against SSA deceptions. Building on historical precedent and real-world SSA data, we simulate an attack whereby an SSA operator seeks to disguise spy satellites as pieces of debris. We further develop and evaluate a machine-learning based anomaly detection tool which allows defenders to detect 90-98% of deception attempts with little to no in-house astrometry hardware. Beyond the direct contribution of this system, the paper takes a unique interdisciplinary approach, drawing connections between cyber-security, astrophysics, and international security studies. It presents the general case that systems security methods can tackle many novel and complex problems in an historically neglected domain and provides methods and techniques for doing so.