An Intelligent Risk-Based Authentication Approach for Smartphone Applications
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
Authentication on smartphones is performed at the initial entry, mostly utilizing knowledge-based authentication methods which are fast and convenient. Device-based authentication does not guarantee that the user will utilize effective authentication credentials as many users choose less robust and easy to remember credentials. To reduce the explicit intervention from users and to increase user adoption, implicit authentication should be present. This approach authenticates users based on temporal access patterns to mobile devices, such as modeling the access behavior to applications. This paper presents an intelligent risk-based authentication method based on temporal access behavior to general applications on mobile devices. The risk score is calculated from the modeled pattern on the mobile device and the approach minimizes the required credentials based on the quality of this pattern. The evaluation of the presented method is achieved on real datasets and the results show the effectiveness of the approach. Importantly, the approach requires only a short period of application usage to build the model in addition to adapting to new app usage. Ultimately, the results show that the approach provides a low false acceptance rate and false rejection rate, which enhances its usability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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