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Record W4312925435 · doi:10.1109/mcom.001.2200183

A Security Assessment of HTTP/2 Usage in 5G Service-Based Architecture

2022· article· en· W4312925435 on OpenAlexafffund
Nathalie Wehbe, Hyame Assem Alameddine, Makan Pourzandi, Elias Bou‐Harb, Chadi Assi

Bibliographic record

VenueIEEE Communications Magazine · 2022
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsResearch CanadaEricsson (Canada)Concordia University
FundersMitacsConcordia University
KeywordsComputer scienceHypertext Transfer ProtocolProtocol (science)Security serviceComputer securityArchitectureComputer networkService (business)World Wide WebCryptographic protocolEnterprise information security architectureInformation securityThe InternetCryptography

Abstract

fetched live from OpenAlex

Fifth generation (5G) networks are designed to bring enhanced network operational efficiency to serve a wide range of emerging services. Toward this purpose, 5G adopts a service-based architecture (SBA) that features web-based technologies such as Hypertext Transfer Protocol version 2 (HTTP/2) used for signaling and application programming interfaces (APIs) for service delivery. Several works in the literature have reported that the shift toward the aforementioned technologies brings potential cybersecurity challenges to the 5G network. In this article, we discuss different security features introduced by 5G SBA and explore these security challenges and their solutions in this new architecture. We carefully examine HTTP/2 features, standards, and custom headers, and discuss their security implications in 5G SBA. We comment on the applicability of some known HTTP/2 attacks in 5G SBA in light of the standardized APIs, and discuss the security opportunities and research directions brought by this protocol and its related technologies.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.750
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.028
GPT teacher head0.292
Teacher spread0.264 · 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 designSimulation or modeling
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

Citations19
Published2022
Admission routes2
Has abstractyes

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