An Adaptive Context-Aware Authentication System on Smartphones Using Machine Learning
Bibliographic record
Abstract
The authentication method for unlocking screens is an essential security feature of smartphones to avoid unauthorized access.Various mechanisms have been integrated into smartphones to authenticate users to reduce privacy infringement.Contextual awareness is now a key requirement for mobile computing to make intelligent decisions and provide an adaptable and convenient authentication model.Therefore, in this paper, a contextaware authentication system based on a machine learning technique is proposed.The proposed system takes the user's body postures, location, SMS contents, and ambient environmental conditions as context.To enhance privacy protection, these context factors are selected to guide the authentication process and enable the authentication system to understand users and their surrounding environment.However, to provide the most appropriate authentication method, a machine learning model is designed.The model is trained and tested with the user's context datasets.An appropriate dataset for the machine learning model is generated, and features that affect end-user interaction during the authentication process are identified.A total of 25 responses from smartphone owners were collected to evaluate the proposed system.After conducting a sentiment analysis, we found that 72 percent of users have a positive sentiment regarding the proposed system, which means that context-aware technology helps improve authentication adaptability and provides a convenient authentication method.The performance of the model was tested, and the results show that the proposed model effectively achieves a Mean Absolute Error (MAE) of 1.299, a Root Mean Square Error (RMSE) of 1.437, and an R Square of 76.78.Therefore, the system can improve the reliability and adaptability of the authentication process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".