When Agile Security Meets 5G
Bibliographic record
Abstract
5G is a critical infrastructure that will connect the whole society and bridge other critical infrastructure systems. Thus, cybersecurity emerges as crucial tenet within the 5G pathway. In this article, we discuss the concept of agile security within a 5G infrastructure taking into account two of its major technologies: Mobile Edge Computing (MEC) and Network Functions Virtualization (NFV). In this sense, we first discuss the 5G-driven MEC deployment from a NFV perspective. Secondly, we present the concept of agile security and how it can be embedded in the daily activities of mobile network operators (MNOs). Thirdly, we discuss risk management as a key element of the agile security framework. To illustrate its application, we propose the design of an agile security risk-aware edge server mechanism for 5G driven MEC deployment, which uses multiple thresholds and a load-balancing security control to mitigate the risks of resource exhaustion and violation of the service level agreement (SLA) faced by the edge servers while taking advantage of multiple cloud layers to increase the degree of availability and dependability of the system. Numerical results show that the proposed mechanism is able to keep the risk at lower levels.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".