Cybersecurity Conceptual Framework Applied to Edge Computing and Internet of Things Environments
Bibliographic record
Abstract
The objective of this research was to propose a conceptual cybersecurity framework aimed at guiding developers in generating and implementing technological solutions for Edge Computing and Internet of Things (IoT) environments. The framework integrates NIST standards and SecDevOps practices, and was developed based on an extensive literature review, synthesizing evidence-based knowledge to offer a comprehensive perspective on actions necessary to address cybersecurity challenges in these environments. The core element of the framework, Govern, led to four primary components: Identity, Protect, Detect, and Respond and Recover. Each component outlines specific actions for identifying cybersecurity vulnerabilities, implementing strategies, and prioritizing privacy and integrity requirements. In order to establish a solid theoretical foundation of the proposal, the framework was conceptually validated through a qualitative method for collecting feedback from a panel of 35 experts from industry, government, and academia. Evaluators confirmed the framework’s relevance, highlighting its integration of NIST standards and SecDevOps practices. This combination is regarded as offering a modular and effective approach for aligning cybersecurity practices with governance principles, addressing cybersecurity challenges, enhancing compliance readiness, supporting secure development, and fostering resilient architectures in IoT and Edge Computing environments. The findings of this evaluation are perceived as promising, since the proposal is considered potentially beneficial to the field of cybersecurity by providing a structured practical framework that could serve as a foundational tool for strengthening security practices in Edge Computing and IoT environments.
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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".