Adversarial Machine Learning Threats to Spacecraft

• URL: http://arxiv.org/abs/2405.08834v1
• Date: Tue, 14 May 2024 02:42:40 GMT
• Title: Adversarial Machine Learning Threats to Spacecraft
• Authors: Rajiv Thummala, Shristi Sharma, Matteo Calabrese, Gregory Falco,
• Abstract summary: As reliance on autonomy grows, space vehicles will become increasingly vulnerable to attacks designed to disrupt autonomous processes. This paper aims to elucidate and demonstrate the threats that adversarial machine learning (AML) capabilities pose to spacecraft.
• Score: 1.837431956557716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spacecraft are among the earliest autonomous systems. Their ability to function without a human in the loop have afforded some of humanity's grandest achievements. As reliance on autonomy grows, space vehicles will become increasingly vulnerable to attacks designed to disrupt autonomous processes-especially probabilistic ones based on machine learning. This paper aims to elucidate and demonstrate the threats that adversarial machine learning (AML) capabilities pose to spacecraft. First, an AML threat taxonomy for spacecraft is introduced. Next, we demonstrate the execution of AML attacks against spacecraft through experimental simulations using NASA's Core Flight System (cFS) and NASA's On-board Artificial Intelligence Research (OnAIR) Platform. Our findings highlight the imperative for incorporating AML-focused security measures in spacecraft that engage autonomy.

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