One‐class SVM for biometric authentication by keystroke dynamics for remote evaluation
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
Abstract Remote skills assessment in distance education needs individual identification in distinguishing between candidates and impostors. Keystroke dynamics is a behavioral biometrics which can be used to identify them. To expect lower error rate, behaviors should be as natural and consistent as possible. The unique identifier assigned to students at their registration seems appropriate but the classification method applied for this case of anomaly detection must be robust even with a lower signature number. In this paper, we first explain how we construct our own dataset. Three methods of selecting Gaussian kernel parameters for one‐class support vector machine are subsequently studied regarding the targeted application constraints. The results show that an indirect method as distance to farthest neighbor cannot be used because some signature features have multimodal and dispersed distributions. A method is then proposed based on the selection of the parameters via detecting the "tightness" of the decision boundaries and uses a greedy search. Its performances are compared to those of a grid search method using LibSVM. The results show that the proposed method is more robust when the signatures number decreases and better and more stable in detecting impostors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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