Contextual, Behavioral, and Biometric Signatures for Continuous 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
Continuous authentication in the Mobile Internet of Things should be based as broadly as possible, since a wide range of factors continuously reveal unexpected correlations. Such factors may include captured events (e.g., password, fingerprint, application start and end, network connect, and disconnect), continuous time series (e.g., gesture, typing rate, accelerometer, GPS, ambient sound, light levels, and time-of-day), and derived behavioral features (e.g., user sociability, browser and application menus, application choice). All these factors have been shown to correlate with the actual user identity, often in surprising combinations. More and more sensors are being deployed in autonomous devices, smart environments and vehicles, enabling even further behavioral and contextual data to be analyzed. The pegs of this continuous authentication “big tent” are moving out further than ever before, bringing it closer to practical uses in our everyday lives.
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 | 0.000 | 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