Optimal Security Risk Management Mechanism for the 5G Cloudified Infrastructure
Why this work is in the frame
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Bibliographic record
Abstract
This work proposes an optimal security risk management mechanism to holistically minimize the risks of a Denial of Service (DoS) attack and Service Level Agreement (SLA) violations that might unfold at the 5G edge-cloud ecosystem. Using the Semi-Markov Decision Process framework, a cyber risk-aware controller is designed to optimally decide on the admission, placement, and migration of a service taking into consideration a user taxonomy and the service requirements. A new cost structure that balances the targeted security risks as well as the cost and the reward of a secure service provisioning is introduced to pave the way for a safe edge-cloud operation. To proactively restrict the population of untrusted users, we consider security controls in the form of a linear and an exponential cost functions and show that the former represents a more flexible and profitable pathway for a Mobile Network Operator to operate at the expense of an inflated security risk while the latter leads to the opposite outcome. Results show that the baseline mechanism might violate the SLA and expose the edge and the cloud to a DoS attack in levels that are 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> , and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> times higher than those of the proposed controller.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it