Keystroke-based authentication by key press intervals as a complementary behavioral biometric
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
Analysis of keystroke dynamics can be useful in protecting personal data because an individual is authenticated not only by password, but also by that individual's keystroke patterns. In this way, intrusion becomes more difficult because the username/password pair, as well as the typing speed and correct keystroke pattern must both be duplicated. The purpose of this paper is to present a keystroke analysis tool that can be incorporated into distributed systems and web-based services. This study also assesses the potential of keystroke analysis as a complementary authentication mechanism. Eleven individuals entered a password into specially developed keystroke analysis software twenty times over a course of four sessions. The data were statistically analyzed to determine keystroke patterns. Tests were performed to verify whether the users could be properly authenticated. Results show that authentication with mean key press timings resulted in very good false acceptance rates, while allowing access to appropriate users.
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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 | 0.000 |
| 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