Adoption of Cybersecurity Capability Maturity Models in Municipal Governments
Bibliographic record
Abstract
Cyberattacks are increasing in diversity and volume placing information and communications technology (ICT) as well as physical assets at risk.Municipal governments operating as the provider of an interdependent network of e-government services, Information and Communications Technology, and Critical Infrastructure (CI) have a requirement to quickly, easily, and inexpensively secure their ICT systems and physical assets.In addition to other sources, this study sampled data from Canadian municipal government CIO's using expert interviews followed by a web-based survey in the winter of 2015 to help inform both the development of a Cybersecurity Capability Maturity Model for Canadian municipalities and to provide recommendations pertaining to the adoption of such a model. The study confirmed the low level of recognition by Canadian municipalities regardingCybersecurity Capability Maturity Models (CCMM) and identified a need to improve their observability in this sector in order to foster adoption.Simplicity of the CCMM and its Trialability in municipal CI networks emerged as key factors influencing its adoption and these factors are considered in the development of the CCMM.Compatibility of the CCMM did not emerge as a factor in adoption as they are new to municipal CI protection.A model combining controls from ISO 27000 and maturity scoring based on SEI-CMMI maturity levels is developed to simplify cybersecurity readiness maturity assessment, and a model for diffusing the CCMM in Canadian municipalities is provided.
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 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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".