Assessing Security in the Multi-Stakeholder Premise of 5G: A Survey and an Adapted Security Metrics Approach
Bibliographic record
Abstract
The fifth-generation (5G) mobile telecom network has been a focal point of research amongst the community in recent times.Features and characteristics like dynamicity (which enables the dynamic allocation of resources to stakeholders on a need basis), low latency, higher network capabilities etc., makes 5G attractive to telecom operators.But as is the case with any new technology, new threats have emerged and made their way into the ecosystem.Therefore, we have a need to evaluate these threats and evaluate the state of security of 5G deployments.Security metrics provide a pathway to quantitatively measure the security posture of an environment by taking these new threats into account.Existing literature contains a plethora of security metrics proposals designed for different system setups.We need to analyze if and how existing security metrics can be applied to a 5G environment.As such, this thesis aims to do a state-of-the-art survey to explore the diverse set of security metrics in literature, understand the process of deriving security metrics.We then employ this knowledge to investigate the factors that hinder the usage of existing security metrics in a 5G environment, identify solutions from existing academic proposals on how to address those factors, propose an adapted security metrics approach to address the multi-ownership nature of 5G and discuss the design and analysis of Argus-5, a tool designed to verify the feasibility of the adapted security metrics approach and simulate various scenarios.Dr. AbdelRahman Abdou for their feedback and suggestions during the oral examination, which helped me to further strengthen the quality of my thesis and Dr.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".