Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions
Yasamin Fayyaz, Li Yang, Khalil El-Khatib

TL;DR
This paper reviews cybersecurity challenges for CubeSats, emphasizing resource-efficient intrusion detection solutions like TinyML, and discusses future research directions for resilient space systems.
Contribution
It highlights the limitations of current cybersecurity practices for CubeSats and explores TinyML as a promising approach for resource-constrained intrusion detection.
Findings
Identified gaps in existing cybersecurity methods for CubeSats.
Proposed TinyML as a resource-efficient intrusion detection solution.
Outlined future research directions for resilient CubeSat cybersecurity.
Abstract
CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education. However, their reliance on Commercial Off-The-Shelf (COTS) components and open-source software has introduced significant cybersecurity vulnerabilities. Ensuring the cybersecurity of CubeSats is vital as they play increasingly important roles in space missions. Traditional security measures, such as intrusion detection systems (IDS), are impractical for CubeSats due to resource constraints and unique operational environments. This paper provides an in-depth review of current cybersecurity practices for CubeSats, highlighting limitations and identifying gaps in existing methods. Additionally, it explores non-cyber anomaly detection techniques that offer insights into adaptable algorithms and deployment strategies suitable for CubeSat constraints. Open research…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
