Cybersecurity KPIs in Higher Institutions: A Systematic Review
Bibliographic record
Abstract
This study systematically reviews the cybersecurity KPIs in the higher education industry. With the evolving technological landscape, higher institutions are facing significant advantages from teaching and learning methods impacting all the stakeholders. However, on the other side they are facing several cyber security threats that have led several stakeholders of higher institutions to face losses and repercussions. The ever-increasing threats and malpractice brings the attention for higher education institutions to adopt effective cybersecurity KPIs. This study aims to focus on assessing the evolution of the research topic and its trends and documenting the rising awareness, implications, and challenges of adopting cybersecurity KPIs within higher institutions. Through a detailed analysis of a systematic literature review, it highlights the various adoption of KPIs within higher education institutions and their outcomes. We use a systematic study from 12 papers between the years 2011 to 2023 that were a part of inclusion criteria which supports the study of this paper. Furthermore, it discusses the stakeholder's implication and the importance of considering cybersecurity KPI by all stakeholders and not just the IT department, hence it needs to be implemented and measured by the rest of the stakeholders in order to achieve it successfully. The findings of this study recommend 15 KPIs of cybersecurity, keeping the model of Kirkpatrick's as a guide developed by previous researchers. The KPIs are chosen based on the systematic review and are recommended by future authors to conduct a survey based on the content validity method in order to validate that the developed KPIs are effective and suitable to be used by higher education institutions in the future.
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 | 0.017 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.034 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".