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 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 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.
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 Internet of Things (IoT) is involved in almost every industry, paving the way for innovative applications, including smart cities, intelligent systems, smart homes, smart agriculture, healthcare, and more. However, due to their asset value, these applications are under continuous and evolving cyberattacks. Furthermore, the advent of quantum computing presents a severe threat to traditional cryptographic methods that typically secure IoT systems, potentially rendering existing security mechanisms ineffective against quantum-enabled attacks. To tackle quantum-enabled and traditional cyberattacks, post-quantum cryptography (PQC) has emerged as a promising solution for providing future-proof security for IoT devices and networks against the quantum computing threat. PQC algorithms can resist cyberattacks from classical and quantum computers, ensuring long-term security solutions. This article explores the integration of PQC into practical IoT systems. It highlights the vulnerabilities of classical cryptographic methods and the necessity of transitioning to quantum-resistant solutions. Moreover, the implementation of PQC on IoT systems is analyzed. By proactively exploring the opportunities and challenges associated with PQC, this research lays out future directions for implementing robust, scalable, and secure IoT systems.
The rapid proliferation of Internet of Things (IoT) devices has revolutionized digital communication, yet it simultaneously poses unprecedented security challenges due to constrained device capabilities and increasing cyber threats. Conventional cryptographic techniques, while currently effective, are vulnerable to emerging quantum computing technologies that threaten to render classical encryption obsolete. This paper explores the integration of quantum-safe cryptographic techniques within IoT networks to ensure long-term confidentiality, integrity, and authentication. It examines post-quantum cryptographic primitives, including lattice-based, hash-based, multivariate, and code-based schemes, and evaluates their suitability for resource-constrained IoT environments. The study focuses on lightweight implementation strategies, balancing computational efficiency with robust security requirements. Simulation analyses demonstrate that properly optimized quantum safe protocols can provide resilient encryption, key exchange, and digital signature functionalities without overwhelming device resources. Furthermore, the integration of these techniques with IoT communication standards and secure network protocols ensures end-to-end protection across heterogeneous networks. Challenges such as latency, energy consumption, and scalability are addressed, highlighting the necessity of tailored cryptographic solutions for diverse IoT applications. The findings confirm that quantum safe cryptography is essential for future-proofing IoT networks, offering sustainable security measures that remain resilient against both classical and quantum adversaries, thereby supporting the continued expansion of secure and intelligent interconnected systems.
The Internet of Things (IoT) now underpins critical systems like healthcare monitors, smart home, smart grids, automotive V2V & V2X networks, and industrial controls but it still relies on classical cryptography, whose security assumptions are vulnerable to quantum computers. A large quantum computer could run Shor’s algorithm to break RSA/ECC and use Grover’s algorithm to accelerate brute-forcing of symmetric keys, undermining the confidentiality and authentication of data in these IoT applications. Researchers have proposed quantum cryptographic solutions. Quantum key distribution (QKD) and quantum random number generators (QRNGs) exploit quantum mechanics to detect eavesdropping and deliver provably secure key material. For example, chip-scale QRNGs can be embedded in IoT sensors to generate true random keys, boosting resistance to brute-force attacks. These quantum techniques provide information-theoretic secrecy, for instance, QKD-generated keys have been used to authenticate smart-grid SCADA. However, most IoT devices are resource-constrained, making direct deployment of conventional QKD hardware challenging. Post-quantum cryptography (PQC) complements quantum methods by providing algorithms such as lattice-based, hash-based that resist quantum attacks on existing hardware. Standardized PQC can be deployed via software updates on legacy IoT nodes, enabling scalable and quantum-safe security within current constraints. Researchers are exploring lightweight PQC for connected cars and e-health sensors. Hybrid approaches combining QKD/QRNG with PQC offer a layered defense. Since QKD protocols are regarded as ‘future-proof’ and PQC standards mature, their combined use will be essential for robust, quantum-safe IoT security. Miniaturizing quantum components such as integrated QKD/QRNG modules and optimizing lightweight PQC schemes for low-power IoT devices are future research directions to secure IoT networks.
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.