Optimized Provisioning Techniques for Geo-Distributed SDP-Enabled Next Generation Networks Security
Bibliographic record
Abstract
What advancements might Next Generation Networks (NGN) unleash that existing ones cannot? NGNs are envisioned to empower the connection between billions of people and zillions of heterogeneous Internet of Things (IoT) devices while consolidating intelligence and autonomy. However, several security-related challenges will be introduced and apparently, the security solutions and architectures used in previous network generations will not be sufficient. The Cloud Security Alliance's (CSA) Software Defined Perimeter (SDP) is a potential candidate to provide the much-needed security framework for next-generation networks. However, the lack of a scalable SDP controller will be a considerable drawback for the wide adoption of the SDP framework. Therefore, this paper focuses on modeling a multi-SDP controller placement problem as a VNF-FGE in a Geo-distributed NFV-based environment as a potential solution to secure next-generation networks. Due to its NP-hard nature, this type of problem can be addressed by extending the NCO approach via Reinforcement Learning (RL) to optimize the reward policy in accordance with the constraints of the problem. The agent developed can learn the placement decisions of the SDP controllers by inference (i.e., policy strategy) through the RL process. The experiment's analysis reveals the RL approach's superiority over the well-known Gecode optimization solver.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".