SUP: Security User Profiles for Behavioral Data Platforms
Bibliographic record
Abstract
Integrating user behavior across domains to create comprehensive profiles is crucial for system efficiency and security. This paper delves into the use of data analytics techniques to gather user data from various application domains, enabling the creation of security user profiles that contain data suitable for user authentication. Specifically, these methods are capable of identifying patterns in the users' data and then using said patterns to detect legitimate but anomalous activities performed by the users. Furthermore, we propose an innovative mechanism that dynamically integrates these profiles to create a comprehensive and global security profile for user authentication. Typically, each profile represents a distinctive entity detailing infrequent user behaviors that significantly impact identity verification. The verification process involves posing user challenge questions generated from these integrated profiles. The generated user profiles ensure the selection of questions that meet both security and usability requirements. We uphold security by issuing each question from the integrated user profiles only once, protecting user responses from potential compromise. We prioritize usability by using fresh data, which helps legitimate users recall and complete the challenge easily.
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How this classification was reachedexpand
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 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".