Cybersecurity Issues in Space Optical Communication Networks and Future of Secure Space Health Systems
Bibliographic record
Abstract
Space is an extreme environment and as a result, unmanned and manned missions rely on the operations of the spacecraft/space habitat, and/or the health of the astronaut(s) in that extreme setting for the success of the mission and the safe return to Earth of the astronauts. Advances in Internet of Things (IoT) enabled sensor devices, Big Data pipelines and artificial intelligence (AI) present new opportunities for real-time monitoring of various spacecraft/space habitat equipment (i.e., hardware), systems (i.e., software) and the environment along with human health. While the Big Data pipeline and AI could exist solely on the spacecraft/space habitat, the provision of a communication network with suitable bandwidth, enables the transmission of raw and derived data streams for additional processing, review and research on Earth. It also provides a different layer of redundancy for onboard AI systems. Free space optical communication links (e.g., laser communication) can support greater bandwidth for exchanging space health data than classic RF communication links. Therefore, the extra provided bandwidth makes these emerging technologies excellent candidates for supporting shorter response times to urgent adverse health situations in space. The lower latency experienced in the exchange of space health data using laser transceivers can further improve the security and well-being of astronauts as well as space occupants, which are considered essential components for successful and scalable manned space missions. That transmission to Earth is at risk of cyber at-tack. In addition, to transmit data through Space, one must consider the jurisdiction and relevant bodies to oversee data traffic management in Space. In this work, we discuss the role of Space Traffic Management (STM) in improving the reliability and security of optical space communication links. Specifically, we discuss different cyber threats that adversely affect the real-time delivery of space health data that are communicated over such links. Furthermore, we propose a series of security controls that any practical Space Traffic Management scheme must take into consideration to ensure the reliability and safety of optical communicating parties/links beyond Low Earth Orbit (LEO).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".