A hybrid-based filtering approach for user authentication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Big data based user authentication is a new approach that leverages the power of Big Data analytics to develop a fertile field for the next generation user authentication. This new approach relies on “something you do”-based verification methods, where the users' dynamic behaviors are analyzed in order to generate real-time uniquely identifiable information about them. Once the unique user's identification is generated “authentication on demand” can be achieved through user challenging questions. In this paper, a new model is proposed to generate these users' identifiable information, where the main concepts of creating users' profiles in Recommendation Systems (RS) is used. RS are using the users' profiles to determine the user's preferences so that they can suggest a list of recommendations to other users with similar preferences. However, in the proposed model these profiles are employed to determine the user's personality traits that have a substantial influence on his/her identity verification. Based on these users' profiles the challenging questions will be issued only once to protect the users' responses from being compromised, and will be generated for the most recent user actions to help the legitimate user to easily remember and successfully complete the challenge.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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