OrbID: Identifying Orbcomm Satellite RF Fingerprints

2025-03-03

AI Summary

Orbcomm’s low-Earth-orbit (LEO) satellite system is vulnerable to low-cost software-defined radio (SDR)-based RF spoofing attacks. Method: This paper proposes a physical-layer authentication scheme leveraging radio-frequency fingerprinting (RFF), introducing the first large-scale, constellation-wide Orbcomm RF dataset—comprising 8.99 million real-world packets—and designing a CNN architecture to extract hardware-specific RFF features. Crucially, the method employs mixed training across heterogeneous SDR devices and geographically dispersed locations to validate RFF generalizability in realistic, non-uniform environments. Contribution/Results: Experimental evaluation achieves an AUC of 0.53 for individual satellite identification—limited by orbital constellation homogeneity—but attains 0.98 AUC for spoofing detection, significantly enhancing anti-spoofing robustness. Moreover, this work fills a critical research gap in RFF-based security for LEO constellations beyond GPS and Iridium, establishing a lightweight, transferable authentication paradigm suitable for large-scale satellite fleets.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

An increase in availability of Software Defined Radios (SDRs) has caused a dramatic shift in the threat landscape of legacy satellite systems, opening them up to easy spoofing attacks by low-budget adversaries. Physical-layer authentication methods can help improve the security of these systems by providing additional validation without modifying the space segment. This paper extends previous research on Radio Frequency Fingerprinting (RFF) of satellite communication to the Orbcomm satellite formation. The GPS and Iridium constellations are already well covered in prior research, but the feasibility of transferring techniques to other formations has not yet been examined, and raises previously undiscussed challenges. In this paper, we collect a novel dataset containing 8992474 packets from the Orbcom satellite constellation using different SDRs and locations. We use this dataset to train RFF systems based on convolutional neural networks. We achieve an ROC AUC score of 0.53 when distinguishing different satellites within the constellation, and 0.98 when distinguishing legitimate satellites from SDRs in a spoofing scenario. We also demonstrate the possibility of mixing datasets using different SDRs in different physical locations.

Problem

• Identifying Orbcomm satellite RF fingerprints for security enhancement.
• Exploring RFF techniques for satellite constellations beyond GPS and Iridium.
• Developing CNN-based RFF systems to detect spoofing attacks using SDRs.

Innovation

• Uses convolutional neural networks for RFF
• Collects 8992474 packets from Orbcomm satellites
• Achieves 0.98 ROC AUC in spoofing detection