Keystroke Identification Based on Gaussian Mixture Models
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 computer systems rely on the username and password model to authenticate users. This method is widely used, yet it can be highly insecure if a user's login information has been compromised. To increase security, some authors have proposed keystroke patterns as a biometric tool for user authentication; they can be used to recognize users based on how they type. This paper introduces a novel method that applies GMMs to keystroke identification. The major benefit of this method is the ability to update the user's model each time he or she is authenticated. Therefore, as time goes on, each user model accurately reflects the changes in that user's keystroke pattern. Using this method, a FAR and a FRR rate of approximately 2% was achieved. However, it should be noted that 50% of the test subjects were the traditional "two finger" typists and therefore, this had a disproportionately negative impact on the results
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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