E-Government Cybersecurity Modeling in the Context of Software-Defined Networks
Bibliographic record
Abstract
Currently, information and communication technologies are going in advance, allowing more information to be distributed globally via the internet superhighway. While being interconnected to the virtual world, people are becoming increasingly focused, large, and smart, needing the development of new solutions that will be implemented and robust network security systems. When the information is being handled in the applications, it is vulnerable to attack at every stage, and it is impossible to handle it in a separate manner, as traditional security systems have done. The introduction of software-defined networks (SDN) has provided a novel perspective on data security, since the network may assist in the construction of stable and safe continuity in the context of risks posed by the internet. The structure of SDN, particularly its gradual construction and centralization of network data and mechanisms, has pushed us to consider security from a strategy-practiced standpoint. The major goal of this chapter is to give detailed an overview of the current state-of-the-art in the area of SDN security and its significance in the context of e-government applications.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".