Bibliographic record
Abstract
The remarkable growth in digital data is changing what and how the defense against the unknown will take place. Big data is a technical term used today to represent this massive growth of digital data that's being created from many sources. Organizations have turned their attentions to the deployment of Big Data analytics to gain valuable insights that benefit their businesses within protected and secure environments. Hence, network security protocols, especially authentication protocols, are being re-designed to protect and to deliver the real benefits of this data growth. Contrary to the traditional perspective, in which researchers are focusing on identifying users' identity to protect Big Data-based environments, we have an opposite perspective that the Big Data itself would be the fuel for the next generation authentication. In other word, the main goal of this work is to propose a new framework for user authentication that leverages Big Data analytics. The core idea of this framework is to find out unique patterns of the users' dynamic behaviors. The proposed framework comprised of three main components. Data Security-based Analytics (DSA); describing the best utilization of the high velocity data streams, which is capable for distinguishing data that has security/identification potentials. Human dynamics measure engine; that develops an ambitious transformation from the Big Data characteristics into the relevant human dynamics measures. Big data-driven authentication service; describes the required engines to design software as a service-based authentication model. Our investigation shows that this new approach will help create a highly distributed authentication model, minimizing the storage of secrets, and lesser secret management overhead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".