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

• URL: http://arxiv.org/abs/2502.07779v1
• Date: Tue, 11 Feb 2025 18:59:32 GMT
• Title: Quantum-driven Zero Trust Framework with Dynamic Anomaly Detection in 7G Technology: A Neural Network Approach
• Authors: Shakil Ahmed, Ibne Farabi Shihab, Ashfaq Khokhar,
• Abstract summary: We propose the Quantum Neural Network-Enhanced Zero Trust Framework (QNN-ZTF) for enhanced security. We integrate Zero Trust Architecture, Intrusion Detection Systems, and Quantum Neural Networks (QNNs) for enhanced security. We show improved cyber threat mitigation, demonstrating the framework's effectiveness in reducing false positives and response times.
• Score: 0.0
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• 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.

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