- The paper introduces the Quantum Neural Network-Enhanced Zero Trust Framework (QNN-ZTF) for securing future 7G networks using dynamic anomaly detection.
- The framework leverages Quantum Neural Networks and principles like superposition and entanglement for precise anomaly detection and risk-adaptive policy enforcement.
- Evaluation results show improved anomaly detection precision and dynamic policy enforcement, achieving 87.4% classification accuracy on the evaluation set.
This paper introduces the Quantum Neural Network-Enhanced Zero Trust Framework (QNN-ZTF), a novel cybersecurity model designed to address the challenges posed by 7G and beyond networks. The framework integrates Zero Trust Architecture, Intrusion Detection Systems, and Quantum Neural Networks (QNNs) to enhance security capabilities, focusing on real-time anomaly detection and adaptive policy enforcement.
Key Contributions:
- Quantum-Driven Anomaly Detection: The framework utilizes QNNs to leverage quantum principles like superposition and entanglement for precise anomaly detection. It introduces advanced quantum feature encoding and variational optimization methods to classify and detect complex attack patterns effectively. The CV-QNN framework is tailored for cybersecurity, analyzing network logs and integrating domain-specific metrics for accurate binary classification.
- Dynamic and Risk-Adaptive Policy Enforcement: The system incorporates an adaptive Zero Trust Architecture that uses quantum-enhanced metrics for real-time, risk-based access control. It implements dynamic threshold mechanisms to reduce false positives and optimize anomaly scoring. The access control decisions are determined based on the quantum risk scores.
- Innovative Quantum-Enhanced Micro-segmentation: QNN-ZTF uses QNN-powered anomaly detection to dynamically isolate high-risk network segments, limiting attacker movement.
- Scalable Hybrid Quantum-Classical Architecture: The model addresses computational limitations of Noisy Intermediate-Scale Quantum (NISQ) devices by designing a scalable hybrid architecture.
- Evaluation and Comparative Analysis: The effectiveness of QNN-ZTF is demonstrated through simulations and real-world case studies. The framework reduces false positives and enhances detection accuracy by leveraging quantum feature encoding and dynamic threshold calibration.
- Threshold Optimization and Sensitivity Analysis: The anomaly detection threshold is optimized to balance sensitivity and specificity. Quantum-enhanced search algorithms accelerate the optimization process.
System Model:
The framework integrates Zero Trust Architecture (ZTF) and Intrusion Detection Systems (IDS) enhanced by QNNs. The IDS implementation employs an advanced QNN architecture for continuous flow monitoring. ZTF operates on the principle of "never trust, always verify," enforcing continuous authentication, authorization, and monitoring. The system incorporates micro-segmentation, dividing the network into smaller, secure segments to minimize the attack surface. A quantum risk score is computed for user-device pairs to determine access permissions. A feedback loop is used to collect misclassified samples and retrain the QNN to reduce false positives and negatives.
QNN-based Solution:
Classical input features are mapped into a quantum state in a higher-dimensional Hilbert space. Quantum superposition enables the simultaneous representation and processing of multiple input states. Entanglement introduces correlations between subsystems in a multipartite quantum state. A variational circuit parameterized by trainable parameters acts on the encoded quantum state to generate a quantum-enhanced representation. Based on the anomaly score, access control policies are dynamically updated. The framework incorporates fully connected quantum layers with Gaussian and non-Gaussian gates. The framework can also embed classical neural networks as a special case. Beyond fully connected architectures, the framework can leverage specialized architectures like Quantum Convolutional Networks, Quantum Recurrent Networks, and Quantum Residual Networks.
Results:
The hybrid quantum-classical ZTF was thoroughly evaluated, incorporating QNNs for dynamic anomaly detection and an adaptive risk-based access control system. The model's accuracy in identifying classifications on the evaluation set reached 87.4%. The results demonstrated improved anomaly detection precision and dynamic policy enforcement.
Conclusion:
The QNN-ZTF presents a novel solution to tackle the challenges of anomaly detection, access control, and adaptive policy enforcement in 7G networks. It enhances intrusion detection systems using quantum principles and reinforces zero trust security by continuously assigning risk scores and isolating high-risk segments. The research establishes a foundation for scalable, next-generation cybersecurity solutions, offering adaptive and resilient defenses against advanced cyber threats.