The European Union and the Protection Of Critical Space Infrastructure from Cyber-Threats: A Strategic Approach?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The functioning of terrestrial critical infrastructures, such as electricity, transportation, and finance depends on critical space infrastructures (CSI). CSI underlie the provision of vital goods and services, economic activities, national and global security. Consequently, securing CSI from cyber-attacks is important to avoid disruptions in the provision of critical goods and services and ensure high levels of security in our societies. Existing cases of cyber-attacks against ground and space components of CSI have proven the consequences of such attacks for domestic and international security, economic, systemic, environmental and social safety and stability. With strategic gains increasingly motivating state and state-sponsored attacks against CSI, the European Union (EU) expanded its resilience and response toolbox to address cyber-threats against CSI. Space has become a highly strategic domain with the EU Strategic Compass, since 2022. Furthermore, in 2023, the High Representative and the Commission put forward an EU Space Strategy for Security and Defence, presenting the EU’s vision for space security. This programmatic document marks a shift in the EU’s configuration of space, from a domain for scientific and civilian enterprises, to one central to security and defence. This paper examines the quick evolution of the EU’s approach to protecting CSI between 2020 and 2024 against the background of the development of the EU’s approach to CI protection more broadly and the development of its space governance aspirations and capabilities. It examines the EU institutional and legislative frameworks for CSI resilience to assesses how relevant and strategic these are considering new technological developments, in the current global security context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it