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.
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.
Abstract The rapid expansion of the Internet of Things (IoT) has produced a highly linked digital environment together with a growing number of complex cyber threats. This work presents a Quantum-Enhanced Hybrid Anomaly Detection Framework combining two advanced Quantum Machine Learning (QML) models: Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) to address these challenges. By projecting data into high-dimensional quantum spaces, QSVM efficiently classifies non-linear and low-frequency threats such as User to Root (U2R) and Probe attacks using quantum kernel functions. Concurrently, QNN models utilize Variational Quantum Circuits (VQCs) to detect large-scale threats like Denial of Service (DoS) and Remote to Local (R2L) attacks by offering superior generalization. Both models produce a final, high-confidence prediction by combining their outputs using a soft voting ensemble approach. The proposed framework guarantees scalability and resilience while ensuring compatibility with present simulation systems. The experimental results obtained indicate that the hybrid QSVM + QNN model significantly outperforms standalone models and conventional methods, providing a future-ready security solution for next-generation IoT infrastructures. In addition, this model delivers a robust, adaptable, and quantum-resilient security solution, well-suited for safeguarding future IoT ecosystems.
With the advancement of quantum computing, the long-term security of IoT systems is increasingly challenged, particularly due to the vulnerability of traditional public-key cryptography. This study integrates artificial intelligence (AI) with a hybrid strategy that involves Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD) to address this challenge. Incoming traffic from the TON-IoT dataset is processed by AI using XGBoost, Random Forest, and One-Class SVM as anomaly-detection filters. Only traffic classified as normal is forwarded to the encryption phase. Postquantum key establishment is performed using Kyber-512, and the resulting shared secret is combined with simulated BB84based QKD key material via a key-derivation function (KDF) to derive a 256-bit session key, which is then used with AES-256GCM for authenticated IoT data encryption. Experimental results demonstrate that the hybrid algorithm, along with the AI, can effectively improve IoT security. Upon optimization of the parameterβ(=1.0), the Delivery Purity is 0.92, the Data Delivery rate is 0.80, and the Secure DeliveryFβscore is 0.94 compared to Delivery Purity of 0.77, Delivery Rate of 1.0, and Secure Delivery Fβ score of 0.87, respectively, without AI.
The emergence of quantum computing seriously threatens the security of legacy cryptographic protocols in consumer electronics devices based on the Internet of Things (IoT). These are essential components of mission-critical infrastructures, such as industrial control systems, smart grids, and healthcare. In particular, this work examines how to strengthen the resistance of the IoT to classical and quantum-based cyberattacks by combining deep learning methods with post-quantum cryptography (PQC). To protect key exchange, encryption, and authentication processes in IoT-consumer devices, we will continue to use quantum-resistant cryptographic algorithms, including lattice-based, hash-based, and code-based cryptography. To provide real-time anomaly detection and response capabilities, we suggest a hybrid security framework combining PQC with deep learning-based intrusion detection systems. Our methodology ensures secure communications and data exchanges linked to quantum attacks in the future within IoT systems while leveraging continuous learning over time to allow Intrusion Detection System (IDS) models to adapt to new attack vectors. We suggest optimized algorithms for PQC solutions that are lightweight and suitable for deployment in edge computing nodes and Internet of Things devices, considering the resource-constrained IoT situations where PQC implementation would be difficult. Through comparative studies and simulations, we demonstrate how our integrated PQC-deep learning framework offers an improved IoT security solution that could safeguard vital systems in a world powered by quantum technology. The results shows the key generation time which is 12.5ms, encryption/decryption time 25.3ms, latency 18.7ms, throughput 5000 operations/sec and energy consumption 2.4mJ.
The proliferation of large-scale Internet of Things (IoT) networks introduce significant challenges for real-time anomaly detection, particularly due to the massive volume, high speed, and heterogeneity of IoT-generated data. Traditional and deep learning-based models, while effective to some extent, often face computational bottlenecks that limit their scalability and responsiveness in complex IoT environments. To address these limitations, this paper investigates the application of Quantum Machine Learning (QML) for anomaly detection in IoT systems. By leveraging the parallelism and high-dimensional processing capabilities of quantum computing, QML offers a promising pathway to overcome current computational constraints. We propose a novel anomaly detection framework based on Quantum Generative Adversarial Networks (QGANs) designed to enhance security, reliability, and real-time responsiveness across diverse IoT scenarios, including smart homes, industrial IoT, healthcare monitoring, and critical infrastructure protection. Furthermore, this paper provides a comprehensive analysis of existing anomaly detection challenges, practical IoT applications, and outlines future research directions, including hybrid quantum-classical architectures, resource-efficient quantum algorithms for edge computing, and the integration of QML with emerging technologies to ensure scalable, secure, and robust IoT systems.
As quantum machine learning (QML) continues to evolve, it promises unparalleled computational advantages in processing complex data. However, the rise of QML also introduces critical concerns regarding data security and privacy, particularly in sensitive domains such as healthcare, finance, and defense. Classical cryptographic methods fall short in addressing threats that arise in quantum communication and computation environments. To bridge this gap, this paper presents a hybrid framework that integrates quantum cryptography—specifically Quantum Key Distribution (QKD)—with QML pipelines, ensuring end-to-end quantum-secure machinelearning operations. We first construct a theoretical model that outlines how QKD can be effectively embedded into a typical QML workflow to mitigate adversarial threats such as eavesdropping, model inversion, and poisoning attacks. We then implement this framework using IBM’s Qiskit and a simulated QKD environment via QuNetSim, applying it to a quantum support vector machine (qSVM) classifier. The integration is evaluated based on accuracy, computational overhead, and communication latency. Our results indicate that quantum-secured QML systems can maintain robust model performance while significantly enhancing data confidentiality. This work lays the groundwork for future developments in secure quantum artificial intelligence infrastructures.
The rapid expansion of the Internet of Things (IoT) has introduced unprecedented cybersecurity challenges, as traditional cryptographic methods struggle to provide both efficiency and quantum resistance in resource-constrained environments. This research introduces Adaptive Quantum Cryptography (AQC), a novel security framework that integrates Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), Quantum Random Number Generators (QRNGs), and AI-driven dynamic adaptation to safeguard IoT networks against evolving cyber threats. Unlike classical encryption techniques such as RSA and ECC, which are vulnerable to quantum attacks, AQC leverages quantum mechanical principles—such as superposition, entanglement, and the no-cloning theorem—to ensure provably secure key exchange and data encryption. The proposed AQC system dynamically adjusts cryptographic protocols based on real-time network conditions, computational resources, and threat intelligence, optimizing security without imposing excessive overhead. Experimental evaluations demonstrate that AQC achieves a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum attacks compared to conventional encryption methods. Additionally, the integration of QRNGs enhances cryptographic key entropy, while AI-driven anomaly detection enables proactive threat mitigation. This framework is particularly suited for next-generation IoT applications, including smart grids, autonomous vehicles, and industrial automation, where security, scalability, and energy efficiency are critical. By combining quantum-resistant algorithms with adaptive security mechanisms, AQC provides forward secrecy, resistance to quantum adversaries, and robust authentication, ensuring long-term protection for IoT ecosystems in the post-quantum era. The findings underscore AQC’s potential as a foundational security architecture for future quantum-safe IoT networks.