The rapid growth of consumer Internet of Things (IoT) networks is transforming industries while introducing complex security challenges, particularly in the era of quantum computing, which threatens traditional encryption methods. Post-quantum cryptography (PQC) provides quantum-resistant protection, yet static deployment often fails to meet the dynamic and resource-constrained requirements of IoT systems. To address this, we propose an intelligent and adaptive security framework that integrates lattice-based PQC with a graph neural network (GNN) and a deterministic soft actor-critic (DSAC) algorithm. The GNN continuously monitors network activity to detect anomalies and provides real-time threat intelligence to the DSAC agent, which dynamically adjusts PQC parameters to optimize security. Simulation results demonstrate that the proposed framework can improve security by up to 40% while reducing computational cost and latency by 27% and 46%, respectively, highlighting the potential of AI-driven, post-quantum cryptography for securing dynamic IoT networks.
The rapid growth of consumer Internet of Things (IoT) networks is transforming industries while introducing complex security challenges, particularly in the era of quantum computing, which threatens traditional encryption methods. Post-quantum cryptography (PQC) provides quantum-resistant protection, yet static deployment often fails to meet the dynamic and resource-constrained requirements of IoT systems. To address this, we propose an intelligent and adaptive security framework that integrates lattice-based PQC with a graph neural network (GNN) and a deterministic soft actor-critic (DSAC) algorithm. The GNN continuously monitors network activity to detect anomalies and provides real-time threat intelligence to the DSAC agent, which dynamically adjusts PQC parameters to optimize security. Simulation results demonstrate that the proposed framework can improve security by up to 40% while reducing computational cost and latency by 27% and 46%, respectively, highlighting the potential of AI-driven, post-quantum cryptography for securing dynamic IoT networks.
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 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.
The rapid proliferation of Internet of Things (IoT) devices presents critical cybersecurity challenges, as traditional cryptographic methods often fail to provide both robust security and operational efficiency in resource-constrained environments. This paper introduces Adaptive Quantum Cryptography (AQC), a novel framework designed to secure next-generation IoT networks against evolving classical and quantum-computing threats. The AQC framework synergistically integrates Quantum Key Distribution (QKD) for unbreakable key exchange, Quantum Random Number Generators (QRNGs) for high-entropy key generation, and Post-Quantum Cryptography (PQC) for quantum-resistant data encryption. A key feature of the system is its dynamic adaptation mechanism, which leverages artificial intelligence to intelligently select and adjust cryptographic protocols in real-time based on current network conditions, device computational capabilities, and identified threat levels. This ensures optimal security without imposing excessive overhead on IoT devices. Our comprehensive experimental evaluation demonstrates that the AQC framework significantly outperforms classical techniques, achieving a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum-based attacks. By providing forward secrecy and robust authentication, the proposed AQC framework offers a scalable, energy-efficient, and future-proof security solution, paving the way for resilient and trustworthy IoT ecosystems in the post-quantum era.
The integration of Artificial Intelligence (AI) in Internet of Things (IoT) financial systems has ushered in a new era of autonomous decision-making, real-time analytics, and dynamic financial interactions. However, the advent of quantum computing presents an imminent threat to the cryptographic foundations of these systems, jeopardizing data confidentiality, transaction integrity, and system authenticity. This paper explores quantum-resistant cryptographic mechanisms—particularly lattice-based, hash-based, and multivariate polynomial cryptography—as viable defenses against quantum attacks on AI-powered IoT financial networks. The study reviews the vulnerabilities introduced by quantum algorithms such as Shor’s and Grover’s, assesses the performance and scalability of post-quantum cryptographic (PQC) protocols in real-world IoT financial deployments, and presents an optimized framework that balances cryptographic strength with computational feasibility. A hybrid security architecture is proposed, integrating PQC with AI-based anomaly detection to ensure end-to-end trust, resilience, and compliance with emerging quantum-secure standards. Through simulations and case-based evaluations, the findings underscore the critical importance of immediate cryptographic transition planning to safeguard future financial infrastructures.
This paper presents lightweight Post-Quantum Cryptography (PQC), identifying its importance for a shift from traditional cryptographic schemes, vulnerable to quantum threats, to efficient PQC algorithms. Lattice-based cryptography stands out owing to its small key sizes and computational efficiency, with CRYSTALS-Kyber and NTRU algorithms being substantial representatives for Internet of Things (IoT) applications. However, PQC implementation in IoT environments has various obstacles to overcome. Minimizing energy consumption, scalability, and hardware limitations remain key challenges for PQC smooth integration into these resource-constrained networks. The present review analyzes the state-of-the-art PQC, makes security and performance comparisons among leading algorithms, and evaluates optimization techniques aimed at reducing resource overheads. Algorithmic refinement, hardware acceleration, and hybrid cryptography are also discussed as methods for mitigating these challenges. The results indicate that continuous research and development efforts should be made to improve the PQC technologies, and thus achieve their practical deployment in IoT systems. Quantum threats in IoT will be, hence, prevented with the employment of secure and scalable IoT ecosystems in a post-quantum world.
The rapid deployment of Internet of Things (IoT) devices across critical infrastructure’s sectors such as healthcare, transportation, and smart cities brings unprecedented security challenges; particularly as quantum computing threatens the classical cryptography. This systematic review analyzes IoT security in relation to post-quantum cryptography (PQC), highlighting vulnerabilities, evolving threat, and cryptographic countermeasures. Using the PRISMA 2020 protocol, our work employed a rigorous and reproducible search strategy, where the initial Boolean query, ((“IoT” OR “Internet of Things”) AND (“Cryptography” OR “Encryption Scheme”) AND “Quantum”), was developed and effectively customized for nine reputable databases to maximize the coverage. From an initial pool of 5,151 publications dated between 2020 and February 12, 2025, a content-based screening was conducted at the title, abstract, and full-text levels. This meticulous filtering resulted in a final corpus of 161 studies deemed relevant for in-depth analysis and synthesis. Then, we categorize and analyze PQC algorithms by vulnerabilities, suitability for resource-constrained IoT environments, performance evaluation, scalability, and quantum resilience. We also identify key limitations in implementations of current cryptographic schemes, including issues in key management and authentication; and outline major challenges such as resource constraints of IoT devices, environmental noise, quantum reversible computing, security vulnerabilities, quantum hardware constraints, and complexities in integration. To counter these threats, we propose a framework of PQC countermeasures and illustrated by a hypothetical case study of PQC adoption in a real-world IoT system. Finally, we propose future research directions in lightweight PQC, hybrid frameworks, improved key management and distribution, addressing quantum distance matters, countering quantum threats, and standardization for IoT environments.