Abstract

In recent years, the space domain has exploded with both commercial and government activity, making situational awareness critically important. In this work, we highlight the need for signal intelligence capabilities that can transfer gracefully from terrestrial systems to space networks. This already difficult task is additionally complicated by the fact that satellites operate at multiple different orbits. Geostationary (GEO) satellites face significantly different channel conditions than their Low Earth Orbit (LEO) counterparts, meaning that any cross-orbital signal analysis tool must be able to handle widely varying conditions. Such tools may soon be critical for identifying satellites and detecting malicious cyber activities. To address this challenge, this research investigates the transferability of different automatic modulation classification techniques. We explore whether feature-based machine learning techniques generalize more effectively than deep-learning models due to their reduced reliance on the original signal and its associated channel effects. Specifically, we compare a simple one-dimensional Convolutional Neural Network (CNN) trained on raw signal data to a Linear Discriminant Analysis (LDA) model trained on features. We use datasets from multiple domains, including terrestrial (RadioML 2018.01A), LEO (Iridium and Orbcomm), and GEO (COMS-1).

Publication Date

4-29-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Cybersecurity (MS)

Department, Program, or Center

Cybersecurity, Department of

College

Golisano College of Computing and Information Sciences

Advisor

Hanif Rahbari

Advisor/Committee Member

Sumita Mishra

Advisor/Committee Member

Michael Gartley

Campus

RIT – Main Campus

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