An Accurate Fraud Source Path Identification Using Integration of Graphical Neural Networks, Long-Short Term Memories, and XGBoost
Bibliographic record
Abstract
With the increase in web usage in terms of e-commerce and financial transactions, cybercriminals would attempt fraud for the benefit of money, and exploit vulnerabilities through multiple channels, in which identifying the true source path is a challenge.The traditional and existing approaches had high false positives and delayed investigation of static forensic behavior tracings.To detect the source path, a multi-layered hybrid model is required that consists of data preprocessing, extraction of behavior features, and an integrated set of graphical neural networks (GNNs), long-short term memories (LSTMs), and XGBoost approaches for real-time identification.In this, reconstruction of paths using GNNs, making temporal analysis using LSTMs, and applying a meta-classifier using XGBoost.For enhanced interpretability, the Explainable AI technique Shapley Additive exPlanations (XAI SHAP) is applied.The models were evaluated based on publicly available transaction datasets, anonymized cross-platform logs, and institutional support.The effectiveness of the proposed model against methods observed to be better in terms of accuracy, reduced false positives, and faster source path tracing, using evaluation measures such as accuracy and area under the curves (AUCs).The source path identification depends on the device used, network forensics, and behavioral biometrics as practices adapted in the proposed model as key stimuli.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".