Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.07779 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- As cyber threats become more complex, modern networks struggle to balance security, scalability, and computational efficiency. While quantum computing offers a promising solution, adoption is limited by scalability constraints, inefficiencies in data encoding, and high computational costs. To address these challenges, we propose the Quantum Neural Network-Enhanced Zero Trust Framework (QNN-ZTF), integrating Zero Trust Architecture, Intrusion Detection Systems, and Quantum Neural Networks (QNNs) for enhanced security. Leveraging superposition, entanglement, and variational optimization, QNN-ZTF enables real-time anomaly detection and adaptive policy enforcement. Key contributions include a hybrid quantum-classical architecture for scalability, dynamic anomaly scoring for improved detection accuracy, and quantum micro-segmentation to contain threats and restrict lateral movement. Evaluation results show improved cyber threat mitigation, demonstrating the framework's effectiveness in reducing false positives and response times. This research establishes a scalable, adaptive, and quantum-optimized cybersecurity model, advancing quantum-enhanced security for next-generation networks.