The emergence of quantum computing challenges traditional security mechanisms, particularly in heterogeneous and resource-constrained IoT and smart environments that must satisfy DN25 requirements. This work introduces a reinforcement learning-driven model for the adaptive selection and orchestration of cryptographic algorithms. Acting as an intelligent decision layer, the system observes contextual, network, and operational metrics to recommend or enforce configurations combining classical schemes, post-quantum cryptography, and Quantum Key Distribution when available. The selection problem is formulated as a Markov Decision Process with state and action spaces aligned with protocol control flows and is embedded into a security middleware with negotiation and fallback mechanisms to ensure interoperability and policy compliance without modifying application logic. Experimental results demonstrate that the model dynamically adjusts key lengths, algorithm families, and security policies according to risk and resource conditions, increasing post-quantum cryptography and Quantum Key Distribution usage by up to 33.4% and 23.9% in high-risk scenarios while favoring low-latency classical or hybrid options for less critical traffic. The system achieves success rates above 78% while maintaining stable latency and resource usage during extended operation.
The emergence of quantum computing challenges traditional security mechanisms, particularly in heterogeneous and resource-constrained IoT and smart environments that must satisfy DN25 requirements. This work introduces a reinforcement learning-driven model for the adaptive selection and orchestration of cryptographic algorithms. Acting as an intelligent decision layer, the system observes contextual, network, and operational metrics to recommend or enforce configurations combining classical schemes, post-quantum cryptography, and Quantum Key Distribution when available. The selection problem is formulated as a Markov Decision Process with state and action spaces aligned with protocol control flows and is embedded into a security middleware with negotiation and fallback mechanisms to ensure interoperability and policy compliance without modifying application logic. Experimental results demonstrate that the model dynamically adjusts key lengths, algorithm families, and security policies according to risk and resource conditions, increasing post-quantum cryptography and Quantum Key Distribution usage by up to 33.4% and 23.9% in high-risk scenarios while favoring low-latency classical or hybrid options for less critical traffic. The system achieves success rates above 78% while maintaining stable latency and resource usage during extended operation.
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.
Abstract The imminent threat posed by quantum computing to classical cryptographic systems necessitates the development of quantum-resistant, efficient, and scalable encryption techniques, especially for real-time distributed optimization networks critical to national infrastructure. This study introduces a novel lightweight post-quantum cryptographic algorithm tailored for secure real-time decision-making in decentralized systems such as smart grids, autonomous transport, and defense communication networks. This study proposes a lattice-based encryption scheme optimized for low-latency and bandwidth-constrained environments, integrating a parameterized Learning With Errors (LWE) framework with a compressed key encapsulation mechanism (KEM). The cryptographic algorithm is coupled with an adaptive distributed optimization protocol that dynamically adjusts computation and communication loads across agents to maintain performance under cryptographic overhead. The method is rigorously analyzed in terms of computational complexity, security assumptions, and operational feasibility. Simulation experiments are conducted over dynamic networks modeled on real-world distributed control systems, comparing performance metrics such as encryption latency, decision throughput, and fault tolerance against state-of-the-art schemes. Results demonstrate significant improvements in end-to-end delay, with cryptographic integrity maintained under adversarial conditions, establishing the scheme’s applicability for post-quantum real-time systems. The contributions of this work bridge the critical research gap between post-quantum cryptography and real-time optimization, reinforcing secure decision-making in systems of national interest.
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 many forms of wireless communication used these days have created a greater demand for strong and flexible cryptographic methods to protect from new security risks that can happen as quantum computing advances.Diffie-Hellman and RSA approaches have been basic to cryptography, but work on these can now be weakened by attack methods such as Shor's and Grover's algorithms following quantum developments which undermines many post-quantum schemes.In our research, we develop a plan using Quantum Machine Learning (QML) to help ensure the security of future wireless networks.As a result, this design pairs quantum theory with reinforcement learning to create strong keys that cannot be accessed by attackers.Value is drawn from quantum entanglement and random measurements of qubits to create key materials needed for physically unclonable keys and at the same time, reinforcement agents support optimizing agreement policies based on variations detected in the channel.Simulations were run using Rayleigh fading and multi-SNR using Qiskit and TensorFlow.The system achieved entropy greater than 95%, agreed on important issues more than 98% successfully and brought latency down to just 0.13 seconds, outshining both classical and post-quantum cryptography in performance, flexibility and safety from attacks.Moreover, the presented encryption solution managed to resist hackers, even if the environment was noisy.As a quantum-AI combination, it is well-positioned to serve as a platform to safely communicate over wireless networks.It builds the basis for examining embedded QML hardware versions of IoT, vehicle networks and smart grid communications, since fast security and reliable performance without high delay are required.
Quantum computing is a threat to the existence of classical cryptographic authentication. Although quantum key distribution (QKD) has quantitative security, its real-life implementation is interfered with due to two reasons: the resource scalability and the availability of an authenticated classical channel. The presented paper proposes a new system, termed as Adaptive Hybrid Authentication Framework (AHAF), as a solution to these issues as it switches dynamically between classical, post-quantum, and quantum authentication protocols. An AHAF is based on a Reinforcement Learning (RL) based decision engine that transforms the problem of protocol selection into a Markov Decision Process (MDP). A multi-objective reward function is maximised by the RL agent, where the security posture, resources consumption in the system and performance are balanced according to real-time threats intelligence and resource availability. The AHAF was structured and verified in some high-fidelity simulation environment that incorporates NetSquid and NS-3. The findings clarify that the AHAF system is a capable solution that prevents a variety of simulated threats and meets the hypothesis of expected uptime (99.9 percent). This is achieved by carefully assigning expensive quantum protocols on a need-to-use basis and so providing QKD level of protection without incurring prohibitive performance cost of a fixed implementation. We have determined that an RL-based learning strategy most readily delivers workable and competitive road map towards feasible quantum-safe communication networks.
The emergence of quantum computing is a serious threat to traditional cryptographic algorithms, making it critical to transition to post-quantum cryptography (PQC). This work proposes a holistic conceptual and analytical framework for deploying PQC in multi-cloud environments where the complexity of different infrastructure, workloads and key management schemes pose significant security and efficiency problems. The proposed framework for crypto-agility follows a layered approach, with a cryptographic abstraction layer, a policy-based orchestration engine, a centralized hardware security module (HSM) management plane, and an ongoing monitoring module to facilitate a smooth transition from classical to hybrid and fully quantum-resistant cryptographic systems. He researches identifies and critically analyzes major challenges in deploying PQC technologies in multi-cloud environments, such as inter-cloud interoperability, computational complexity, latency, key management, and regulatory and compliance considerations. The framework tackles these challenges by incorporating hybrid cryptographic protocols that blend classic cryptographic algorithms with NIST-approved PQC algorithms, namely ML-KEM (FIPS 203) and ML-DSA (FIPS 204), to maintain backward compatibility and increase security against quantum-powered adversaries. Moreover, the framework proposes dynamic policy-driven algorithm selection to implement context-sensitive cryptographic enforcement based on sensitivity labels, security threats, and regulatory considerations. An experimental framework is proposed to assess the efficiency and scalability of PQC algorithms deployed in a multi-cloud environment, including metrics such as CPU usage, memory usage, latency and throughput with different workloads. Although this research is conceptual, it draws on previous benchmarking to discuss the securityperformance trade-offs associated with PQC. The results highlight the need for centralized trust anchors, crypto-agility and hybrid deployment models to address risks from cryptographic diversity and ensure the sustainability of security operations. This study advances the research on quantum-safe cloud security by offering a systematic, scalable and policy-oriented framework for PQC deployment. It provides practical guidance for businesses, cloud architects, and security professionals on how to safeguard against quantum vulnerabilities and ensure efficient operations in distributed systems by implementing quantum-safe cryptographic infrastructures.