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Minimum Requirements for Space System Cybersecurity - Ensuring Cyber Access to Space

2024· article· en· W4405601621 on OpenAlexaff
Gregory Falco, Nicolò Boschetti, Arun Viswanathan, Brandon Bailey, Carsten Maple, Güneş Karabulut Kurt, Johannes Willbold, Jill Slay, Edward Birrane, David L. Logsdon, William C. Ferguson, James Curbo, Jacob G. Oakley, Moritz Schloegel, Johan Sigholm, Cameron Mehlman, Rajiv Thummala, Matteo Calabrese, Yogita Shah, Anhtuan Le, Kymie Tan, Erin Miller, Gregory Epiphaniou, Ugur Ilker Atmaca, Wayne Henry, Gürkan Gür, Riccardo Vecellio Segate, Olfa Ben Yahia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsPolytechnique Montréal
FundersNational Aeronautics and Space Administration
KeywordsComputer securityComputer scienceSpace (punctuation)Cyber SpaceCyber threatsThe InternetOperating system

Abstract

fetched live from OpenAlex

Space systems are continuously under cyber attack. Minimum cybersecurity design requirements are necessary to preserve our access to space. This paper proposes a scalable, extensible method for developing minimum cyber design principles and subsequent requirements for a space system based on any given mission priority. To test our methodology, we selected the fundamental mission priority of preserving access to space by preventing the permanent loss of control of a satellite. We then generate the minimum number of secure-by-design principles that can collectively prevent the permanent loss of control of a satellite and translate these into example minimum requirement ‘shall’ statements. Our proposed minimum requirements methodology and example can serve as a starting point for policymakers aiming to establish security requirements for the sector. Further, our methodology for establishing minimum requirements will be engaged for prioritizing the efforts of the emergent IEEE International Technical Standard for Space Cybersecurity (Working Group P3349).

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.324
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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