Keypress interval timing ratios as behavioral biometrics for authentication in computer security
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
Many different types of keystroke dynamics approaches have been explored to protect personal data in networked systems. Keystroke patterns are behavioral biometrics, and are considered to be as unique to an individual as a signature. This paper presents a new approach to keystroke analysis that uses key press interval ratios to authenticate users. Participants in this study registered their passwords into a specially-designed analysis program. Keypress ratios were calculated, and neural network techniques were employed to obtain a mapping between patterns and the correct user. Results indicate that authentication through keypress ratios achieves high true acceptance rates, while also maintaining low false acceptance rates, which are particularly important in high-security applications. The approach presented here is suitable for incorporation into agent-based networked security systems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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