Virtualized security at the network edge: a user-centric approach
Bibliographic record
Abstract
The current device-centric protection model against security threats has serious limitations. On one hand, the proliferation of user terminals such as smartphones, tablets, notebooks, smart TVs, game consoles, and desktop computers makes it extremely difficult to achieve the same level of protection regardless of the device used. On the other hand, when various users share devices (e.g., parents and kids using the same devices at home), the setup of distinct security profiles, policies, and protection rules for the different users of a terminal is far from trivial. In light of this, this article advocates for a paradigm shift in user protection. In our model, protection is decoupled from users' terminals, and it is provided by the access network through a trusted virtual domain. Each trusted virtual domain provides unified and homogeneous security for a single user irrespective of the terminal employed. We describe a user-centric model where nontechnically savvy users can define their own profiles and protection rules in an intuitive way. We show that our model can harness the virtualization power offered by next-generation access networks, especially from network functions virtualization in the points of presence at the edge of telecom operators. We also analyze the distinctive features of our model, and the challenges faced based on the experience gained in the development of a proof of concept.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".