Machine learning for mobile network payment security evaluation system
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
Abstract In the recent past, different types of Mobile Network payments gateways have been explosively growing, which allows consumers to access services using various types of mobile devices. The demanding, challenging factors in the mobile network payment gateway security evaluation system includes malware detection, multi‐factor authentication, and fraudulent detection in payment systems. In this paper, Machine Learning‐Assisted Secure Mobile Electronic Payment Framework (ML‐SMEPF) is proposed to detect the presence of malware, authentication issues, and fraud detection in mobile transactions. Here, the Efficient Random Oracle Model is introduced to detect the presence of malware on a host system and multi‐factor authentication challenges posed during mobile payments. Mutual Mobile Authentication model is incorporated with ML‐SMEPF, to identify the type of fraud detection which ensures a safe and secure mobile payment platform. The simulation analysis is performed based on accuracy ratio, security factor, performance, and cost factor proves the reliability of the proposed framework.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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