Approaches for cybersecurity education: a South African military science perspective
Bibliographic record
Abstract
Cybersecurity threats have escalated in South Africa, highlighting vulnerabilities across government, industry, and national infrastructure. This paper is a position article that synthesises and critiques literature and institutional practices to propose optimal, actionable approaches for cybersecurity education in a South African military context. This paper aims to identify optimal approaches for cybersecurity education in South Africa, with focused recommendations for the Faculty of Military Science and other higher education institutions. A position paper methodology synthesises recent literature, policy documents, and the National Initiative for Cybersecurity Education (NICE) curricular framework, evaluating educational models, student profiles, industry engagement, and active learning tools relevant to the South African context. Effective cybersecurity education requires multidisciplinary curricula, integration of technical and social science perspectives, practical exercises (like CTF and cyber ranges), and strong collaboration with industry and government. Student profiles should emphasise both technical acumen and soft skills. South African needs are best addressed by mapping local competencies to international frameworks while contextualising to national security priorities. Curricula must be updated to reflect multidisciplinary competencies, provide practical training, align with international standards, and foster industry/government partnerships to build resilience and professional capacity in South Africa’s cybersecurity workforce.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".