Abstract The rapid expansion of Internet of Things (IoT) infrastructures has heightened their exposure to sophisticated adversaries, including those equipped with quantum capabilities. This work introduces a hybrid framework that integrates post-quantum cryptography (PQC), quantum key distribution (QKD), and quantum machine learning (QML) to deliver quantum-resilient anomaly detection. A novel PQC-IoT dataset is generated using real-time Raspberry Pi sensors, enriched with 30+ features spanning 12 traffic classes (9 attacks, 3 benign), and incorporating PQC-specific metadata such as encryption latency and ciphertext size. Security is enforced through Kyber-512 encryption and a BB84-based QKD channel, while anomaly detection is performed using a quantum support vector machine (qSVM). Experimental results show that qSVM consistently outperforms classical SVM and XGBoost by 4--7% in accuracy, precision, recall, and F1-score, achieving up to 97.6% accuracy with encryption overhead limited to 5--7%. The framework maintains a Quantum Bit Error Rate (QBER) below 11% under noise models, validating its robustness. To the best of our knowledge, this is the first reproducible benchmark that unifies PQC, QKD, and QML into a single pipeline, offering a practical pathway toward quantum-secure IoT anomaly detection.
Basavarajappa et al. (Thu,) studied this question.