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Record W4399920733 · doi:10.34190/eccws.23.1.2300

The U.S. National Cybersecurity Strategy: A Vehicle with an International Journey

2024· article· en· W4399920733 on OpenAlexaboutno aff
Jami Carroll

Bibliographic record

VenueEuropean Conference on Cyber Warfare and Security · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer securityAeronauticsNational securityPolitical scienceBusinessComputer scienceEngineeringLaw

Abstract

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The U.S. National Cybersecurity Strategy is focused on the five pillars of defending critical infrastructure: detect, disrupt, and dismantle threat actors; improve market resilience and security; invest in future resilience; and create international partnerships with shared goals. The National Cybersecurity Strategy Implementation Plan is focused on critical infrastructure supporting energy, financial, healthcare, information technology, and manufacturing sectors. In the U.S. alone, the SolarWinds supply chain attack affected nine federal agencies and about 100 companies. Ransomware attacks such as the Colonial Pipelines, the largest U.S. oil pipeline, disrupted supplies of gasoline and fuel to the U.S. East Coast and the JBS USA as the largest meat processor ransomware attack affecting one-fifth of the nation’s meat supply. The U.S. National Cybersecurity Strategy as a response to the U.S.’s critical infrastructure concerns led to the creation of two core cybersecurity documents which were crafted jointly with several other allies. Cybersecurity and Infrastructure Security Agency (CISA) crafted the Shifting the Balance of Cybersecurity Risk: Principles and Approaches for Secure by Design Software with joint agreement with National Security Agency (NSA), the Federal Bureau of Investigation (FBI), and 15 international government agencies to give international vendors a roadmap of the expected cybersecurity hygiene required from their products. (CISA, 2023a; Car & De Luca, 2022) Building on the Shifting the Balance of Cybersecurity Risk: Principles and Approaches for Secure by Design Software, CISA, FBI and NSA met with cybersecurity organizations from Australia, Canada, New Zealand, and United Kingdom and jointly created The Case for Memory Safe Roadmaps: Why Both C-Suite Executives and Technical Experts Need to Take Memory Safe Coding Seriously as a core issue identified in the earlier guidance. (CISA, 2023c). These led by the U.S. helped initiate an international cybersecurity norm insisting international software manufacturers demonstrate product security and transparency. They showed how a global community can rally to solve cybersecurity challenges that have existed for decades. This led to twenty of the largest international software vendors creating the Minimum Viable Secure Product (MSVP) Working Group to address the requirements levied by these documents; CISA has joined this working group to help shape procurement, contractual controls, self-assessment, and system development lifecycle (SDLC) with these vendors. (CISA, 2024d; MSVP, n.d.) This research argues that the U.S. National Security Council (NSC) should leverage the talent pool of CISA, National Institute of Standards and Technology (NIST), Department of Defense (DoD), FBI, and NSA to improve detection, information sharing, security standards, and implementation for not only the U.S.’s government and commercial sectors, but also helps our allies and partners. The DoD and Office of the Director of National Intelligence (ODNI) have made great strides in improving security by integrating improvements with Zero Trust Architecture (ZTA), Supply Chain Risk Management (SCRM), Software Supply Chain Security, Cybersecurity Safety Review Board (CSRB), Cybersecurity Incident & Vulnerability Response Playbooks, and DoD National Security Systems (NSS) standards. The NSC should coordinate through CISA to develop a collaborative effort to not only benefit the U.S. critical infrastructure but also help our allies and partners.

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)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0080.005
Scholarly communication0.0210.013
Open science0.0010.007
Research integrity0.0120.012
Insufficient payload (model declined to judge)0.0210.006

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 head0.055
GPT teacher head0.328
Teacher spread0.273 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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