Improving cyber defence for critical national infrastructure in New Zealand
Bibliographic record
Abstract
The challenge of securing comprehensive services enabled by cyber-physical technologies is becoming increasingly acute. Industrial Control Systems (ICS) and Operational Technology (OT) environments have been in place for several decades. With a combination of computer software, hardware components and industrial/commercial use, these systems are essential in the control and automation of countless industrial procedures and processes that provide indispensable human services in most countries; they make it possible to operate and maintain such operations as the flow of energy through power grids, the treatment and supply of clean water to billions of people, and the maintenance of life saving medical facilities around the world. This research aims to critically analyse New Zealand's existing cybersecurity strategies and approaches in its defence of Critical National Infrastructure (CNI) organisations operating OT and ICS environments. In this regard, the research draws on international best practices, and proposes a set of hypotheses and actionable insights to fortify cyber resilience for CNIs. It also explores how government-enforced frameworks and standards improve cyber defence for CNIs along with improved accountability. Learnings from this research may be used by policy makers, cyber security leaders, and the government of New Zealand in their consideration of and consultations on academic and pragmatic application, for the development or adoption and enforcement of cyber security standards for CNIs in New Zealand. The essence of this thesis lies in its commitment to contributing to the broader discourse on cybersecurity for OT and ICS environments--particularly in safeguarding critical infrastructures--thereby enhancing the security and welfare of nations in a dynamically changing threat landscape. To achieve the aforementioned aim, this thesis undertakes an analysis of cyber security standards and frameworks that governments around the globe--especially within the countries represented in the Five Eyes intelligence alliance comprising Australia, Canada, New Zealand, the United Kingdom, and the United States--have enforced for CNIs operating OT and ICS environments. Additionally, the thesis examines whether there are other geographies that are a closer fit culturally and economically for New Zealand to learn from and to emulate when it comes to considering future strategies for improving cyber defence for CNIs. This thesis further explores how systematic, strategic, and collaborative efforts in combination with government enforced frameworks and standards improve cyber defence for CNI and OT and ICS environments. It is guided by comparative analysis, utilizing both qualitative and quantitative data, including policy document reviews, expert interviews, and studies of international best practices.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".