Duth: a user‐friendly dual‐factor authentication for Android smartphone devices
Bibliographic record
Abstract
Abstract With the pervasiveness of smartphones and the richness of mobile apps, many people are storing increasingly sensitive data on them, in greater quantities. In order to protect this sensitive information from misuse due to loss, or other accidental reasons, strong smartphone authentication has become imperative and has received considerable attention in recent years. However, when we directly implement traditional authentication schemes in smartphone devices, the balance between security and user‐friendliness of authentication becomes challenging, mainly because of the input‐in‐motion environments. In this paper, without adding extra hardware devices, we present a user‐friendly, dual‐factor authentication scheme called Duth, for Android smartphone devices. Specifically, the proposed Duth scheme is characterized by utilizing the spatial and time features of the user‐writing process as two factors of authentication; a user can be authenticated only if these two features are fulfilled. We implement Duth in Java as a library, which we make publicly available. With extensive discussions on parameter selection, we choose proper parameters and implement Duth on a smartphone with Android 2.3 for experiments, and the experiment results demonstrate that Duth can indeed achieve efficient and effective dual‐factor authentication. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".