A dynamic model building process for virtual network security assessment
Bibliographic record
Abstract
Network virtualization - in which network topologies and protocols are tailor-made for individual service providers across multiple infrastructure providers - is a concept that holds great promise for the future internet. However, security in the Virtual Network (VNet) context is difficult to assess and understand because service providers have no visibility into the infrastructure over which their networks operate which could be a significant concern from an adoption perspective. In a previous work, we introduced a VNet Security Assessment Process to address this challenge by building a security preference model based on the input of a group of security experts. However, a flexibility-limiting factor of the process is the requirement for security experts to meet each time a model change is required. In this paper, we introduce DS-MACBETH which combines Dempster-Shafer theory (DST) with the multi-criteria decision making process MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique). We combine DST with MACBETH in order to allow security experts to contribute to model building in an asynchronous, distributed fashion. We integrate DS-MACBETH into our previous VNet security assessment process to achieve a dynamic security model building process whose sources of knowledge can be expanded beyond human sources of security knowledge (e.g. sensors, expert systems, etc).
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".