Quantum-driven Zero Trust Framework with Dynamic Anomaly Detection in 7G Technology: A Neural Network Approach

TL;DR

This paper introduces a quantum-enhanced zero trust cybersecurity framework that uses quantum neural networks for real-time anomaly detection and adaptive policy enforcement in 7G networks, improving threat mitigation and response efficiency.

Contribution

It presents a novel hybrid quantum-classical architecture integrating quantum neural networks with zero trust principles for scalable, adaptive cybersecurity in next-generation networks.

Findings

Enhanced threat detection accuracy

Reduced false positives and response times

Effective quantum micro-segmentation

Abstract

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.