Threat-Oriented Digital Twinning for Security Evaluation of Autonomous Platforms

TL;DR

This paper introduces a modular digital twin framework for cybersecurity testing of autonomous systems, enabling realistic threat simulation and evaluation across UAV and space applications.

Contribution

It provides a reproducible, threat-oriented design pattern for testing autonomous platforms' security against various cyber threats.

Findings

The methodology enables observable, controllable security tests.

The architecture is adaptable to UAV and space systems.

The approach facilitates dependable autonomy research.

Abstract

Open, unclassified research on secure autonomy is constrained by limited access to operational platforms, contested communications infrastructure, and representative adversarial test conditions. This paper presents a threat-oriented digital twinning methodology for cybersecurity evaluation of learning-enabled autonomous platforms. The approach is instantiated as an open-source, modular twin of a representative autonomy stack with separated sensing, autonomy, and supervisory-control functions; confidence-gated multi-modal perception; explicit command and telemetry trust boundaries; and runtime hold-safe behavior. The contribution is methodological: a reproducible design pattern that translates threat analysis into observable, controllable tests for spoofing, replay, malformed-input injection, degraded sensing, and adversarial ML stress. Although the implemented proxy is ground based, the architecture is intentionally framed around stack elements shared with UAV and space systems, including constrained onboard compute, intermittent or high-latency links, probabilistic perception, and mission-critical recovery behavior. The result is an implementable research scaffold for dependable and secure autonomy studies across UAV and space domains.