The U.S. National Cybersecurity Strategy: A Vehicle with an International Journey
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".