Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2026
|
| Online Access: | https://doi.org/10.5281/zenodo.20391671 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866902139242020864 |
|---|---|
| author | M. Satya Vijaya J. Rinithya T. Venkata Nandhini K. Sharlee B. Rushitha |
| author_facet | M. Satya Vijaya J. Rinithya T. Venkata Nandhini K. Sharlee B. Rushitha |
| contents | <p class="MsoNormal"><strong><span>Cyber fraud is still a serious threat to data-driven infrastructures, e-commerce sites, and financial systems. It frequently evades detection models that use static rules or traditional machine learning. In order to improve detection accuracy and cost sensitivity, A method is present Q-Defender Net, a hybrid quantum-classical framework that blends ensemble classification and quantum feature selection. After preprocessing the data with normalization and class balancing, the system maps feature into Hilbert space using quantum kernel alignment, and then uses QAOA to identify the most informative features. The parallel classifiers XGBoost and Quantum SVM then process these features, and their outputs are combined using weighted voting.High-value fraud cases are given priority by a cost-conscious loss function, which enhances practical impact. According to experimental results, Q-Defender Net outperforms FD4QC and Hybrid ML in terms of error rate and convergence speed, achieving 99% accuracy, 98% precision, and a 98.5% F1-score. It is a potent remedy for contemporary cybersecurity issues due to its modular, scalable architecture and adversarial robustness.</span></strong></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20391671 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hybrid Quantum-Classical Framework For Cyber Fraud Detection With Quantum Feature Selection Using Q-Defender Net M. Satya Vijaya J. Rinithya T. Venkata Nandhini K. Sharlee B. Rushitha <p class="MsoNormal"><strong><span>Cyber fraud is still a serious threat to data-driven infrastructures, e-commerce sites, and financial systems. It frequently evades detection models that use static rules or traditional machine learning. In order to improve detection accuracy and cost sensitivity, A method is present Q-Defender Net, a hybrid quantum-classical framework that blends ensemble classification and quantum feature selection. After preprocessing the data with normalization and class balancing, the system maps feature into Hilbert space using quantum kernel alignment, and then uses QAOA to identify the most informative features. The parallel classifiers XGBoost and Quantum SVM then process these features, and their outputs are combined using weighted voting.High-value fraud cases are given priority by a cost-conscious loss function, which enhances practical impact. According to experimental results, Q-Defender Net outperforms FD4QC and Hybrid ML in terms of error rate and convergence speed, achieving 99% accuracy, 98% precision, and a 98.5% F1-score. It is a potent remedy for contemporary cybersecurity issues due to its modular, scalable architecture and adversarial robustness.</span></strong></p> |
| title | Hybrid Quantum-Classical Framework For Cyber Fraud Detection With Quantum Feature Selection Using Q-Defender Net |
| url | https://doi.org/10.5281/zenodo.20391671 |