Post quantum blockchain framework using probabilistic hidden state deep learning for smart IoT systems

BELS-IoT: A Blockchain-Integrated Ensemble Learning Framework for Secure and Trustworthy IoT Device Protection

Quantum Resilient Explainable Autonomous IoT Security Framework Using Dual‐Layer Blockchain for Edge Intelligence

The fast explosion of Internet of Things (IoT) technology across smart homes, autonomous vehicles, healthcare, and industrial infrastructure has led to real‐time data transmission, which is highly susceptible to sophisticated cyber‐attacks. The scalability of security protocols, trust management, and robustness against quantum‐based attacks is limited for existing security protocols. To address these challenges, this research proposes a Quantum Resilient Explainable framework with Dual Layer Blockchain (QRE‐DLB) for secure edge intelligence. The proposed framework utilizes CRYSTALS‐Kyber512 and Dilithium2 for quantum‐resistant authentication and communication, whereas dual‐layer blockchain technology is used for decentralized authorization and trust verification, which is achieved through static cryptographic identities and dynamic behavioral analysis. For intelligent threat detection, the trio‐based rethinking transformer‐enhanced temporal convolutional network is used for adaptive anomaly detection, whereas the multi‐view heterogeneous deterministic‐probabilistic graph attention network is used for attack detection. The proposed framework achieves 99.4% detection accuracy, 99.3% precision, 99.2% recall, 24% reduction in detection latency, 18% reduction in energy consumption, and 13.7% improvement in attack mitigation rate. These findings demonstrate the practical applicability of QRE‐DLB for secure edge‐enabled IoT deployments and provide an effective engineering solution for building scalable, explainable, and quantum‐resilient cyber‐physical systems.

QuantumShield-IoT: A Quantum-Resilient Hybrid Framework for Secure Data Transmission Using Quantum Key Distribution, Lightweight Cryptography and Blockchain Technology

With the growing Internet of Things (IoT), many applications like traffic management, smart cities, environment monitoring, industrial automation and healthcare have been revolutionized. As the number of resourcelimited IoT devices grows and connects with each other, security issues have become more intricate and severe, such as unauthorized access, data tampering, replay attacks, and the rise in threat from quantum computing to traditional cryptographic methods. To solve those problems, this paper proposes Quantum Shield-IoT, which is a quantum-resilient hybrid security framework that combines the Quantum Key Distribution (QKD) protocol of BB84 with ASCON lightweight authenticated encryption, blockchain security, and cloud computing to ensure secure end-to-end data transmission in IoT. The BB84 protocol works both to generate quantum secure cryptographic keys, and to identify eavesdropping via Quantum Bit Error Rate (QBER) analysis. The generated keys are used by ASCON-128 to ensure efficient data confidentiality, integrity and authentication with low computation load, which makes it suitable for resourceconstrained IoT devices. Encrypted data are securely stored in the cloud while the data integrity and traceability of the SHA-256 hash values are ensured by being stored on the blockchain, making them irretrievable. Encrypted data are stored securely in the cloud, while the data integrity and traceability of the hash value ensure irretrievable storage through the blockchain. The proposed framework was implemented using IBM Quantum Qiskit, ASCON-128, blockchain platforms and cloud storage. Experimental results show that quantum bit error rate (QBER) varies from 2.1% to 3.5% for typical communication and 24.8% for eavesdropping attempts, proving reliable QKD. ASCON encryption range was between 8.2 ms and 69.5 ms, and blockchain verification was less than 25 ms for 1,000 transactions with more than 99% of the integrity accuracy. Moreover, the framework achieved an attack detection rate higher than 97.9% in several cyberattacks and hence provided a scalable, light-weight and quantum resistant security solution for next generation IoT ecosystems.

Post-Quantum Blockchain and AI-Based Security Framework for Smart Healthcare Systems

The security of smart healthcare systems has become an important research topic as patient records are progressively digitalized, and new cyber threats continue to emerge. Traditional cryptographic mechanisms may become less reliable in the presence of quantum computing capabilities, which motivates the need for quantum-resistant security solutions. A simulation-based human-in-the-loop reference architecture for smart healthcare systems with post-quantum cryptographic mechanisms, blockchain-enabled auditability, and artificial intelligence (AI)-enabled threat detection to enhance security, privacy, and real-time monitoring is proposed. Our proposed framework integrates post-quantum digital signatures, quantum-resistant key establishment mechanisms with tamper-evident blockchain logging, and AI-based anomaly detection to facilitate secure and traceable healthcare data processing. Simulation experiments were performed using synthetic healthcare cybersecurity scenarios involving electronic health record access behavior and Internet of Medical Things traffic (e.g., unauthorized access attempts, anomalous data exfiltration, SQL injection, or distributed denial-of-service (DDoS)-like flooding attacks). The proposed AI-based detection model achieved an accuracy of 92.1%, a precision of 91.4%, a recall of 93.2%, an F1-score of 92.3%, and a false-positive rate (FPR) of 5.1%, with a detection-cycle latency of 200 ms. Scalability was evaluated using up to 10,000 simulated healthcare endpoints, with stable detection performance and blockchain-logging behavior observed in the simulation environment. The results indicate that the suggested framework can offer a scalable and privacy-aware security architecture for next-generation smart healthcare systems. The framework must be treated as a simulation-based reference that needs to be validated against real healthcare datasets and deployment scenarios.   Received: 18 March 2026 | Revised: 26 May 2026 | Accepted: 22 June 2026   Conflicts of Interest The author declares that he has no conflicts of interest to this work.   Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.   Author Contribution Statement Ahmad H. Alenezi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – revie

Integrated IoT ‐Cloud Security Through Markov Decision Processes, Blockchain Authentication and Reinforcement Learning

The pace of the IoT devices growth and their connection with cloud services posed considerable security threats such as DDoS attacks, data breach, and unauthorized access, which undermine the integrity and privacy of the system. The current security solutions are not usually able to deliver adaptive, low‐latency, and reliable security in dynamic IoT‐cloud systems, and a more robust and smarter security model is required. The purpose of this research was to create an end‐to‐end IoT‐cloud security system that is based on markov decision processes, reinforcement learning, and blockchain‐enhanced authentication in order to achieve better attack detection, false alarms, and safe device management. The framework simulated dynamic network states using MDP in the optimal choice, applied RL to keep on enhancing detection policies and used blockchain in the authentication of devices in a non‐tampered manner. The reinforcement learning agent in this framework operates through MDPs by monitoring network states to choose security actions which lead to policy updates based on state‐transition rewards for ongoing threat reduction improvements. Performance was measured using the Bot‐IoT dataset. Experimental findings revealed that it achieved a detection accuracy of 98.00% and reduced the false positive rate to 1.50%, which was much better than the conventional ML and single RL‐MDP model. There was an efficiency improvement of 52.79% reduction in system latency and 48.15% improvement in throughput. The framework offers a scalable, adaptable, and safe system of IoT‐cloud networks to guarantee the integrity of data and resilience of operation.

An In-Depth Control of Blockchain Enabled and AI based Security Mechanisms in Distributed WSN-IoT Environments

This WSNs coupled with the Internet of Things (IoT) is very much needed in facilitating smart environment like smart cities, industrial automation, healthcare monitoring and environmental monitoring. Nevertheless, WSN-IoT systems are extremely susceptible to security risks such as denial-of-service attacks, malicious node behaviors, manipulation of routing, data manipulation, and privacy breaches due to their distributed, heterogeneous, and resource-constrained nature. Conventional centralized security models are usually insufficient to deal with these dynamic and scale cyber threats. The paper describes a detailed overview of blockchain-based and artificial intelligence (AI)-based security solutions to the distributed WSN-IoT scenarios. The paper examines the security dangers at varying layers of a IoT architecture and surveys the recent frameworks that incorporate machine learning, deep learning, and federated learning along with blockchain technology in managing decentralized trust and identify intrusions. Moreover, performance trade-offs on the effectiveness of security, energy use, latency, and scalability are discussed. Lastly, the research indicates the presence of an open challenge and future research topics of creating secure, scalable, and intelligent WSN-IoT infrastructure. The paper also suggests a solution of integrative AI-blockchain security framework that would have a combination of AI-based intrusion detection and decentralized blockchain trust management.