A Multi-Feature User Authentication Model Based on Mobile App Interactions
Why this work is in the frame
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Bibliographic record
Abstract
Knowledge-based authentication approaches such as the use of passwords and personal identification numbers (PINs) are the most common ways of authenticating users. The main problem with such approach is relying on simple authentication login credentials at the login stage, and assuming the user is still the same between access sessions makes applications and networks vulnerable to access by unauthorized users. Application-level access patterns on smartphone and tablet devices can be utilized to provide an approach for continuously authenticating and identifying users. This paper presents a user authentication and identification method based on mobile application access patterns, and throughout the paper we use a smart home environment as a motivating scenario. To enhance the classification process, many features have been extracted and utilized which considerably improved differentiating between users and eliminating similarities in the access usage patterns. The proposed model has been evaluated using two datasets, and the results show an ability to authenticate users with high accuracy in terms of low false positive, false negative, and equal error rates. Overall, the statistical analysis of the extracted multi-features and the results show that the feasibility of decision-making based on app interactions can lead to high accuracy.
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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 | 0.001 |
| 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