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<p>The rapid expansion of the Internet of Things (IoT) has introduced significant cybersecurity and computational challenges that exceed the capabilities of traditional security mechanisms. This study presents a quantum–classical hybrid security model that enhances anomaly detection, encryption, and predictive maintenance in IoT networks. By integrating quantum key distribution (QKD), post-quantum cryptography (PQC), and quantum machine learning (QML), the proposed approach strengthens threat detection and ensures secure communication. Experimental validation using noisy intermediate-scale quantum (NISQ) devices and IBM quantum simulators demonstrates a 98.7% anomaly detection accuracy, an 80% reduction in latency, and a 3.9% false-positive rate, significantly outperforming traditional AI-based intrusion detection models. The quantum federated learning (QFL) framework further enhances decentralised AI accuracy by 14.5%, while QKD improves encryption resilience by increasing the secure key rate by 500%. The model’s ability to reduce training time by 50% and enhance energy efficiency by 225% makes it scalable for real-time IoT deployments. The proposed security model has wide-ranging implications for industries reliant on IoT networks, such as healthcare, smart cities, and industrial automation, where real-time anomaly detection and secure communication are critical. Organisations deploying IoT infrastructure can leverage quantum-enhanced security to mitigate evolving cyber threats, reduce operational risks, and ensure compliance with future post-quantum cryptographic standards. This research establishes a quantum-secured IoT ecosystem, reinforcing post-quantum encryption and real-time quantum security analytics to mitigate evolving cyber threats. Future directions will explore quantum homomorphic encryption (QHE), zero-trust security architectures (ZTSA), and adaptive quantum AI models, ensuring the practical deployment of quantum-enhanced cybersecurity solutions. The findings highlight quantum methodologies as a scalable, computationally efficient, and high-accuracy approach to securing next-generation IoT environments.</p>

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Quantum-driven security evolution in IoT: AI-powered cryptography and anomaly detection

  • 期刊论文

  • 发表日期：

    2025年6月29日

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  • Hana Mohammed Mujlid,
  • Reem Alshahrani

摘要

The rapid expansion of the Internet of Things (IoT) has introduced significant cybersecurity and computational challenges that exceed the capabilities of traditional security mechanisms. This study presents a quantum–classical hybrid security model that enhances anomaly detection, encryption, and predictive maintenance in IoT networks. By integrating quantum key distribution (QKD), post-quantum cryptography (PQC), and quantum machine learning (QML), the proposed approach strengthens threat detection and ensures secure communication. Experimental validation using noisy intermediate-scale quantum (NISQ) devices and IBM quantum simulators demonstrates a 98.7% anomaly detection accuracy, an 80% reduction in latency, and a 3.9% false-positive rate, significantly outperforming traditional AI-based intrusion detection models. The quantum federated learning (QFL) framework further enhances decentralised AI accuracy by 14.5%, while QKD improves encryption resilience by increasing the secure key rate by 500%. The model’s ability to reduce training time by 50% and enhance energy efficiency by 225% makes it scalable for real-time IoT deployments. The proposed security model has wide-ranging implications for industries reliant on IoT networks, such as healthcare, smart cities, and industrial automation, where real-time anomaly detection and secure communication are critical. Organisations deploying IoT infrastructure can leverage quantum-enhanced security to mitigate evolving cyber threats, reduce operational risks, and ensure compliance with future post-quantum cryptographic standards. This research establishes a quantum-secured IoT ecosystem, reinforcing post-quantum encryption and real-time quantum security analytics to mitigate evolving cyber threats. Future directions will explore quantum homomorphic encryption (QHE), zero-trust security architectures (ZTSA), and adaptive quantum AI models, ensuring the practical deployment of quantum-enhanced cybersecurity solutions. The findings highlight quantum methodologies as a scalable, computationally efficient, and high-accuracy approach to securing next-generation IoT environments.