Summary of Adversarial Machine Learning Threats to Spacecraft, by Rajiv Thummala et al.
Adversarial Machine Learning Threats to Spacecraft
by Rajiv Thummala, Shristi Sharma, Matteo Calabrese, Gregory Falco
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates the threats posed by adversarial machine learning (AML) capabilities to spacecraft, highlighting the importance of incorporating AML-focused security measures in autonomous space vehicles. The authors introduce an AML threat taxonomy for spacecraft and demonstrate experimental simulations using NASA’s Core Flight System (cFS) and OnAIR Platform, showcasing the potential attacks on probabilistic processes based on machine learning. By analyzing the execution of AML attacks, the study emphasizes the need to safeguard against disruptions in autonomous systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how bad guys might hack into space vehicles that can make decisions on their own. It’s like someone trying to trick a self-driving car. The researchers want to show what kind of problems this could cause and how we can protect our spacecraft from these attacks. They come up with a list of possible threats and then test different kinds of hacking attempts on NASA’s flight systems. The results are scary, showing just how easy it is to mess with the decisions made by autonomous space vehicles. |
Keywords
» Artificial intelligence » Machine learning
