The convergence of quantum computing and the scale of Internet of Things (IoT) deployments creates a compounded security problem: classical public-key cryptography securing today’s IoT traffic is vulnerable to harvest-now, decrypt-later attacks, while the constrained hardware typical of IoT devices limits the security mechanisms can be deployed. Prior work has largely evaluated post-quantum cryptography (PQC), federated learning (FL)-based anomaly detection, and quantum-resilient storage separately or in partial combinations, leaving open whether these mechanisms remain practical when composed on constrained hardware under a shared operational budget. This paper answers that question with a three-layer architecture — a hybrid PQC pipeline (Kyber-based key encapsulation, ML-DSA signatures, AES-GCM encryption, and PQC-TLS transport), a federated anomaly-detection layer, and a quantum-resilient storage and audit layer (DeSSE and MuReQua) — executed end-to-end on a Raspberry Pi 5 edge node under AWS IoT Greengrass Core, with nine concurrent virtual IoT nodes processing real attack traffic from the N-BaIoT and TON_IoT datasets. Benchmarking across 27 cryptographic algorithm combinations (810 pipeline executions) shows that all 27 configurations completed in under 2.1 ms, with the deployed Kyber768/ML-DSA-65/AES-256-GCM tier adding 4,521 bytes of per-message overhead, which remained compatible with the evaluated 20 ms inter-row transmission budget. Live federated deployment across the nine virtual IoT nodes on N-BaIoT achieves F1-scores above 0.998 on eight of nine devices, processing at approximately 900 rows/second with zero signature verification failures; a centralized baseline evaluation further confirms 99.46% attack recall on unseen TON_IoT threats withheld from training. The quantum-resilient storage layer maintains 100% chain integrity and complete signature coverage across 4,471 audit records, with independent tamper simulations reliably detected. PQC-TLS introduces a one-time handshake overhead of 13.7 ms (288.5% relative increase) over classical TLS, which remains small relative to the multi-minute FL round intervals measured in the evaluated deployment. Together, these results demonstrate that quantum-resilient cryptography, federated intelligence, and auditable storage can be jointly executed on constrained IoT edge hardware as a single experimentally demonstrated pipeline within the operational conditions evaluated in this study.
The convergence of quantum computing and the scale of Internet of Things (IoT) deployments creates a compounded security problem: classical public-key cryptography securing today’s IoT traffic is vulnerable to harvest-now, decrypt-later attacks, while the constrained hardware typical of IoT devices limits the security mechanisms can be deployed. Prior work has largely evaluated post-quantum cryptography (PQC), federated learning (FL)-based anomaly detection, and quantum-resilient storage separately or in partial combinations, leaving open whether these mechanisms remain practical when composed on constrained hardware under a shared operational budget. This paper answers that question with a three-layer architecture — a hybrid PQC pipeline (Kyber-based key encapsulation, ML-DSA signatures, AES-GCM encryption, and PQC-TLS transport), a federated anomaly-detection layer, and a quantum-resilient storage and audit layer (DeSSE and MuReQua) — executed end-to-end on a Raspberry Pi 5 edge node under AWS IoT Greengrass Core, with nine concurrent virtual IoT nodes processing real attack traffic from the N-BaIoT and TON_IoT datasets. Benchmarking across 27 cryptographic algorithm combinations (810 pipeline executions) shows that all 27 configurations completed in under 2.1 ms, with the deployed Kyber768/ML-DSA-65/AES-256-GCM tier adding 4,521 bytes of per-message overhead, which remained compatible with the evaluated 20 ms inter-row transmission budget. Live federated deployment across the nine virtual IoT nodes on N-BaIoT achieves F1-scores above 0.998 on eight of nine devices, processing at approximately 900 rows/second with zero signature verification failures; a centralized baseline evaluation further confirms 99.46% attack recall on unseen TON_IoT threats withheld from training. The quantum-resilient storage layer maintains 100% chain integrity and complete signature coverage across 4,471 audit records, with independent tamper simulations reliably detected. PQC-TLS introduces a one-time handshake overhead of 13.7 ms (288.5% relative increase) over classical TLS, which remains small relative to the multi-minute FL round intervals measured in the evaluated deployment. Together, these results demonstrate that quantum-resilient cryptography, federated intelligence, and auditable storage can be jointly executed on constrained IoT edge hardware as a single experimentally demonstrated pipeline within the operational conditions evaluated in this study.
Industrial innovations, driven by technologies such as the Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI), have revolutionized various operational domains, including supply chain management, equipment monitoring, predictive maintenance, and quality control. Deep Learning (DL) has become instrumental in addressing complex, data-driven challenges within industrial settings, relying on large datasets collected from heterogeneous sources. However, centralizing these datasets on cloud platforms poses significant privacy and security risks. Federated Learning (FL) offers a promising solution by enabling distributed model training across multiple nodes while preventing the exchange of raw data. Despite its advantages, FL introduces new vulnerabilities, especially related to the security of communication channels between participating entities. Traditional cryptographic mechanisms, such as digital signatures and model encryption, can mitigate these risks, but the emergence of quantum computing threatens the robustness of conventional solutions. This work explores the integration of Post-Quantum Cryptography (PQC) into FL to enhance security without incurring significant performance degradation. A modular FL architecture with three security levels is proposed: (i) an unprotected baseline; (ii) authenticated communication using ML-DSA digital signatures; and (iii) full protection combining ML-DSA and ML-KEM-512-based key encapsulation. Implemented within the Flower framework, the architecture is evaluated under simulated MitM attacks. Experimental results demonstrate that PQC-enhanced schemes effectively mitigate quantum-resistant threats while maintaining acceptable computational overhead, thereby ensuring model integrity and data confidentiality.
Quantum computing poses a significant threat to classical asymmetric cryptography, which is essential for ensuring confidentiality, authentication, and key exchange in contemporary digital infrastructures. Although post-quantum cryptography (PQC) provides mechanisms that resist quantum attacks, its implementation in Internet of Things (IoT) systems is challenged by constrained resources, including limitations in computation, memory, energy, latency, and bandwidth, and the heterogeneity of devices. This paper offers a comprehensive narrative review of PQC approaches applicable to IoT, systematically organizing 30 peer-reviewed studies published between 2022 and 2026 across four layers: device, communication, distributed trust, and application. Additionally, the review examines two cross-cutting dimensions, privacy and side-channel resistance. The analysis indicates a significant prevalence of lattice-based schemes, hybrid strategies, and integrations with blockchain technology, zero-knowledge proofs, federated learning, homomorphic encryption, AI, and Zero Trust architectures. Notably, key gaps remain in side-channel evaluation, migration pathways, deployment costs, and real-world validation—issues that are particularly critical given the long lifecycles of IoT devices and the ongoing threat of “harvest now, decrypt later” attacks.
The rapid advancement of quantum computing poses a critical threat to classical cryptographic algorithms such as RSA and ECC, particularly in Internet of Things (IoT) devices, where secure communication is essential but often constrained by limited computational resources. This paper investigates the feasibility of deploying post-quantum cryptography (PQC) algorithms on resource-constrained devices. In particular, we implement three PQC algorithms - BIKE, CRYSTALS-Kyber, and HQC - on a lightweight IoT platform built with Raspberry Pi devices. Leveraging the Open Quantum Safe (liboqs) library in conjunction with mbedTLS, we develop quantumsecure key exchange protocols, and evaluate their performance in terms of computational overhead, memory usage, and energy consumption for quantum secure communication. Experimental results demonstrate that the integration of PQC algorithms on constrained hardware is practical, reinforcing the urgent need for quantum-resilient cryptographic frameworks in next-generation IoT devices. The implementation of this paper is available at https://iqsec-lab.github.io/PQC-IoT/.
The rapid advancement of quantum computing poses a critical threat to classical cryptographic algorithms such as RSA and ECC, particularly in Internet of Things (IoT) devices, where secure communication is essential but often constrained by limited computational resources. This paper investigates the feasibility of deploying post-quantum cryptography (PQC) algorithms on resource-constrained devices. In particular, we implement three PQC algorithms - BIKE, CRYSTALS-Kyber, and HQC - on a lightweight IoT platform built with Raspberry Pi devices. Leveraging the Open Quantum Safe (liboqs) library in conjunction with mbedTLS, we develop quantumsecure key exchange protocols, and evaluate their performance in terms of computational overhead, memory usage, and energy consumption for quantum secure communication. Experimental results demonstrate that the integration of PQC algorithms on constrained hardware is practical, reinforcing the urgent need for quantum-resilient cryptographic frameworks in next-generation IoT devices. The implementation of this paper is available at https://iqsec-lab.github.io/PQC-IoT/.
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.
The rapid growth of the Internet of Things (IoT) is transforming industries such as healthcare, transportation, and smart infrastructure, but it also amplifies critical concerns regarding data security, privacy, and device authentication. Traditional cryptographic mechanisms offer partial protection, yet they remain vulnerable to emerging quantum computing threats, posing significant risks to IoT systems. Addressing these challenges, this paper introduces FLQC-IoT, a novel security framework that integrates federated learning (FL) with quantum computing (QC) to provide robust, scalable, and future-ready protection. Unlike conventional centralized approaches, FLQC-IoT enables decentralized model training directly on IoT devices, safeguarding sensitive data from exposure. Quantum technologies are incorporated at multiple layers: quantum neural networks accelerate optimization tasks, while quantum key distribution and quantum homomorphic encryption ensure tamper-proof key exchange and secure communication. Furthermore, the framework adapts dynamically to emerging threats through real-time anomaly detection and distributed intelligence, significantly enhancing resilience. Experimental results demonstrate a 15.32 % reduction in computational overhead and a 21.45 % decrease in authentication rounds compared to state-of-the-art methods, highlighting the framework’s efficiency and practical readiness. These findings underscore the significance and transformative impact of FLQC-IoT, providing a scalable, privacy-preserving, and resilient approach to securing IoT networks across critical domains, including healthcare, university campus networks, smart infrastructure, and transportation.