Improving Performance and Usability in Mobile Keystroke Dynamic Biometric Authentication
Bibliographic record
Abstract
In the last few years, the number of mobile devices such as smartphones and tablets, in circulation, has increased dramatically. The primary and often only protection mechanism in these devices is authentication using a password or a Personal Identification Number (PIN). Passwords are notoriously known to be a weak authentication mechanism, no matter how complex the underlying format is. A more secure alternative option which has gained interest recently is extracting keystroke dynamic biometrics from supplied passwords for mobile authentication. In this paper, we show that using random forests classifier, improved accuracy performance can be achieved for mobile keystroke dynamic biometric authentication. We also propose a new algorithm for handling typos, which is an essential step in improving usability. We study both timing features and pressure-based features. Experimental evaluation is based on two public datasets and a third dataset collected in our lab. The best performance, obtained by combining timing and pressure features, is an Equal Error Rate (EER) of 2.3% for a population of 42 users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".