Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions

This project is an academic reference implementation associated with a recent research paper. With 0 stars and minimal activity, it currently functions as a proof-of-concept rather than a production-ready tool. The defensibility is low because the code serves primarily to validate the paper's findings, lacking a developer ecosystem or proprietary dataset. However, the niche is highly specialized; frontier labs (OpenAI, Google) have no incentive to compete in space-grade embedded security. The real threat comes from specialized aerospace cybersecurity firms (e.g., SpiderOak, Kayhan Space) or government-funded research (Space Force's Hack-A-Sat) which develop more robust, closed-source versions of these algorithms. The value lies in the domain-specific application of TinyML to satellite-specific protocols (like CCSDS), but until it gains community traction or hardware-in-the-loop validation, it remains a reproducible research artifact.