Secure Optimization of API-Driven Financial Transactions Using Deep Learning: A Threat Detection Framework for Mutual Fund Processing
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
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).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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