Toward secure user-habit-oriented authentication for mobile devices
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
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. Although many user authentication mechanisms have presented in the past, studies have shown mobile users prefer usability over security and, unfortunately, a higher level of security often entails sacrificing usability. Moreover, 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 with user authentication on mobile devices. The user-habit-oriented authentication turns a tedious security action into an enjoyable experience. Also, we propose a rhythm based authentication scheme, providing the first proof of concept toward secure user-habit-oriented authentication for mobile devices. Experimental results show that the proposed scheme has high accuracy in terms of false rejection rate. Also, the proposed scheme is able to protect from attacks caused by credential disclosure, which could be fatal to the traditional schemes.
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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 | 0.001 | 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