Secure Optimization of API-Driven Financial Transactions Using Deep Learning: A Threat Detection Framework for Mutual Fund Processing
Bibliographic record
Abstract
For software applications and systems to interact smoothly and support automated and efficient service delivery, system-to-system communication via Application Programming Interfaces (APIs) is crucial. APIs enable the sharing of data and functions across various platforms, improving both operational performance and user interaction. However, this integration can expose systems to security threats that may be exploited by malicious entities, emphasizing the need to recognize and address related security risks. In this paper, secure optimization of API-driven financial transactions using deep learning a threat detection framework for mutual fund processing (SO-APID-FT-DL-TDF-MFP) is proposed. At first, the input data is taken from the CIC-IDS2017 dataset. Then, the gathered data are fed into the pre-processing segment using implicit unscented particle filter (IUPF) which is used to eliminating noise. The pre-processed data are fed into Gegenbauer graph neural networks (GGNN) for prediction purpose. GGNN is used to predict potential security threats in the API-driven financial transactions by identifying irregular patterns and anomalies in the transaction data, thereby enhancing the overall security of the mutual fund processing system. Then, the proposed method implemented in python and the performance metrics like accuracy, precision, F1-score, recall, receiver operating characteristic (ROC) and specificity analyzed. The proposed SO-APID-FT-DL-TDF-MFP achieves 98% precision, 97% recall, 96% F1-score, 97.1% specificity, 97.5% accuracy, and 1.149 seconds computational time, with a high ROC of 0.99 compared with existing methods, such as adoption of deep-learning models for managing threat in API calls with transparency obligation practice for overall resilience (MT-APIC-TOP-OR-DL), deep learning for intelligent assessment of financial investment risk prediction (IA-FIRP-DL) and fraud prediction using machine learning: the case of investment advisors in canada (FP-CIAC-ML).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".