User-Habit-Oriented Authentication Model: Toward Secure, User-Friendly Authentication for Mobile Devices
Bibliographic record
Abstract
Mobile device security has become increasingly important as we become more dependent on mobile devices. One fundamental security problem is user authentication, and if not executed correctly, leaves the mobile user vulnerable to harm like impersonation and unauthorized access. Although many user authentication mechanisms have been presented in the past, studies have shown mobile users preferring usability over security. Furthermore, mobile users often unlock their devices in public spaces, inevitably resulting in a high possibility of user credentials disclosure. Motivated by the above, we introduce a novel user-habit-oriented authentication model, where mobile users can integrate their own habits (or hobbies) with user authentication on mobile devices. The user-habit-oriented authentication turns a tedious security action into an enjoyable experience. In addition, we propose a rhythm-based authentication scheme, providing the first proof of concept toward secure user-habit-oriented authentication for mobile devices. The proposed scheme also takes the first step toward using the theory of mind into security field. Experimental results show that the proposed scheme has high accuracy in terms of false rejection rate. In addition, the proposed scheme is able to protect from attacks caused by credential disclosure, which could be fatal if it was done through the traditional schemes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".