International Journal of Science, Engineering and TechnologyAuthors: M. Satya Vijaya, J. Rinithya, T. Venkata Nandhini, K. Sharlee, B. Rushitha
Abstract: 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.
DOI: http://doi.org/10.5281/zenodo.20391671
