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Advances in computational intelligence and robotics book series

Challenges and Solutions of Using Quantum Security Frameworks for IoT Devices

Published Jul 16, 2026• 0 citations
Publisher
DOI
10.4018/979-8-3373-3857-6

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The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. To enable several secure, intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.

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Abstract With the emergence of quantum computing, traditional cryptographic algorithms face the threat of being compromised, necessitating the development of quantum-resistant alternatives. The Internet of Things (IoT) paradigm presents unique security challenges due to its vast scale and resource-constrained devices. This research aims to address these challenges by proposing a novel quantum-resistant cryptographic algorithm QuantIoT specifically designed for securing IoT devices. The research begins by evaluating the vulnerabilities of existing cryptographic algorithms against quantum attacks and identifying the need for post-quantum solutions in the IoT context. Various families of post quantum cryptographic algorithms, including lattice based and hash based schemes, are examined to assess their suitability for securing IoT devices. Based on the analysis, a novel quantum-resistant cryptographic algorithm tailored for IoT devices is proposed. The algorithm takes into account the limited computational capabilities, power constraints, and communication requirements of IoT devices while offering strong defence against both conventional and quantum threats. The proposed algorithm is evaluated through simulations and practical experiments on a representative IoT platform. Performance metrics, including computation time, memory usage, and communication overhead, are measured and compared against traditional cryptographic algorithms. The results show that the suggested quantum-resistant technique for protecting IoT devices is workable and effective. This research contributes to the growing body of knowledge on post-quantum cryptography and provides valuable insights for the design and implementation of secure IoT systems in the face of quantum threats.

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This work focuses on the security of mobile payments in Internet of Things networks, with particular emphasis on the integration of post-quantum cryptographic principles. The study adopts a modeling and simulation-based approach to analyze security challenges arising from the advent of quantum computing. The main contributions include an analytical review of post-quantum cryptographic algorithms, the identification of potential vulnerabilities in IoT-based mobile payment systems, and the development of a multivariate mathematical framework to evaluate security-performance trade-offs. Through synthetic data generation and statistical modeling, the proposed framework highlights the impact of cryptographic complexity, network latency, and device resource constraints on transaction security. The results demonstrate how post-quantum-aware security mechanisms can be optimized under practical IoT constraints. The significance of this work lies in its proactive approach to long-term security, addressing the growing vulnerability of traditional RSA- and ECC-based systems in the presence of quantum adversaries. By focusing on abstraction, modeling, and simulation, this study provides insights that support the design of future quantum-resistant mobile payment architectures in IoT environments.
The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. To enable several secure, intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.
Abstract With the emergence of quantum computing, traditional cryptographic algorithms face the threat of being compromised, necessitating the development of quantum-resistant alternatives. The Internet of Things (IoT) paradigm presents unique security challenges due to its vast scale and resource-constrained devices. This research aims to address these challenges by proposing a novel quantum-resistant cryptographic algorithm QuantIoT specifically designed for securing IoT devices. The research begins by evaluating the vulnerabilities of existing cryptographic algorithms against quantum attacks and identifying the need for post-quantum solutions in the IoT context. Various families of post quantum cryptographic algorithms, including lattice based and hash based schemes, are examined to assess their suitability for securing IoT devices. Based on the analysis, a novel quantum-resistant cryptographic algorithm tailored for IoT devices is proposed. The algorithm takes into account the limited computational capabilities, power constraints, and communication requirements of IoT devices while offering strong defence against both conventional and quantum threats. The proposed algorithm is evaluated through simulations and practical experiments on a representative IoT platform. Performance metrics, including computation time, memory usage, and communication overhead, are measured and compared against traditional cryptographic algorithms. The results show that the suggested quantum-resistant technique for protecting IoT devices is workable and effective. This research contributes to the growing body of knowledge on post-quantum cryptography and provides valuable insights for the design and implementation of secure IoT systems in the face of quantum threats.