A New Framework for Publishing and Sharing Network and Security Datasets
Bibliographic record
Abstract
Datasets are very important for network and security research and development. Despite the continuous growth in the number of available datasets, there is no effective publishing and sharing mechanisms so that realistic and representative datasets are not only hard to construct but it is difficult to select from tens of thousands of datasets scattered in online repositories. This work aims to alleviate the difficulties inherent in searching, selecting and comparing datasets as well as to decrease the ambiguity associated with dataset publication and share. In this paper we present the basis and the implementation of a new framework to describe and share network datasets with a special focus on network and security-related datasets. Hereafter, we present the underlying idea of the proposed framework and the key component of this approach: a Dataset Description Language (DDL) to express dataset metadata. Besides that, we explain how we implemented a proof-of-concept prototype to demonstrate its feasibility and usefulness, only from OSOTS (Open Source Off The Shelf). It allows us to overcome the problem of backward dealing with a huge number of already existing datasets where it generates Dataset Description Sheets (DDS) automatically for traffic datasets. The proposed approach provides several benefits where it facilitates searching in dataset repositories according to various criteria. Moreover, its output in XML format can be integrated easily with Security Content Automation Protocol (SCAP) tools. It also, enhances communicating dataset properties in a clear and succinct manner.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".