User Authentication for Smart Home Networks Based on Mobile Apps Usage
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
End-user devices, such as mobile phones and tablets, have become essential tools for accessing smart homes. Consequently, user authentication, one of the most important security factors, needs to be considered to prevent unauthorized access to home devices. Although mobile phones are equipped with different means of authentication such as fingerprint readers, these methods are only employed at the time of access; hence, countermeasures should be developed to overcome potential threats. This paper presents a continuous user authentication model based on apps access usage on mobile devices. To validate the presented model, two public real-world datasets collected from real users over a long period, are used. The model is evaluated for its ability to differentiate between users utilizing shared apps at the same daily intervals. Moreover, various classification approaches regarding legitimate user classification in compliance with the history of apps usage are evaluated. The results demonstrate the capacity of the presented method to authenticate users with high true positive and true negative rates.
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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 | 0.001 |
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