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Record W4409592954 · doi:10.2514/1.i011583

High-Altitude Platform Station Systems Cybersecurity Analysis

2025· article· en· W4409592954 on OpenAlexaff
Nicolò Boschetti, Güneş Karabulut Kurt, Gregory Falco

Bibliographic record

VenueJournal of Aerospace Information Systems · 2025
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPayload (computing)Computer securityComputer scienceArchitectureTransponder (aeronautics)Network architectureSystems architectureComputer networkEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

This paper examines cybersecurity threats in high-altitude platform station (HAPS) systems through reference architecture and attack tree methods. Given the rising commercial and military interest in these systems to enable next-generation 6G and hybrid telecommunication architectures, the threat of cyber and electronic attacks is increasing. The study focuses on providing the complete reference architecture of an aerostatic HAPS system equipped with a hybrid free-space optical and radio frequency transponder payload to be employed as a node of a nonterrestrial network. This study investigates potential attack vectors across various subsystems by coupling the attack tree methodology with the attack surface mapping derived from the reference architecture. Recommendations for mitigating cyberthreats and a secure-by-design approach are proposed to enhance the safety of future HAPS systems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.210
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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