Detection of Aerial Spoofing Attacks to LEO Satellite Systems via Deep Learning

TL;DR

This paper introduces a deep learning-based anomaly detection method using autoencoders to identify aerial spoofing attacks on LEO satellite systems, validated through real-world drone-based experiments at various altitudes.

Contribution

It presents a novel PHY layer spoofing detection technique employing autoencoders and demonstrates its effectiveness against drone-launched attacks, unlike existing methods.

Findings

Reliable detection of spoofing at various altitudes

Outperforms state-of-the-art approaches in experiments

Open source dataset for further research

Abstract

Detecting spoofing attacks to Low-Earth-Orbit (LEO) satellite systems is a cornerstone to assessing the authenticity of the received information and guaranteeing robust service delivery in several application domains. The solutions available today for spoofing detection either rely on additional communication systems, receivers, and antennas, or require mobile deployments. Detection systems working at the Physical (PHY) layer of the satellite communication link also require time-consuming and energy-hungry training processes on all satellites of the constellation, and rely on the availability of spoofed data, which are often challenging to collect. Moreover, none of such contributions investigate the feasibility of aerial spoofing attacks launched via drones operating at various altitudes. In this paper, we propose a new spoofing detection technique for LEO satellite constellation systems, applying anomaly detection on the received PHY signal via autoencoders. We validate our solution through an extensive measurement campaign involving the deployment of an actual spoofer (Software-Defined Radio) installed on a drone and injecting rogue IRIDIUM messages while flying at different altitudes with various movement patterns. Our results demonstrate that the proposed technique can reliably detect LEO spoofing attacks launched at different altitudes, while state-of-the-art competing approaches simply fail. We also release the collected data as open source, fostering further research on satellite security.