Aug 2026· Transactions on Emerging Telecommunications Technologies· Vol 37· 0 citations· 24 references
TL;DR
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 is created.
Abstract
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.
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.
S. Madhuri, D. Dhevi· International Conference Com...· 0 citations
The growth of the Internet of Things (IoT) has brought about security concerns owing to the massive global integration of diverse and constrained devices. Blockchain, Machine Learning (ML) and Deep Learning (DL) techniques have been proposed as effective ways to improve IoT security. This paper reviews the literature on the use of blockchain and smart learning techniques for security enhancement and threat detection in IoT networks. Blockchain offers a decentralized and immutable approach to secure data integrity, authenticating and controlling access to IoT devices, while ML and DL techniques allow intelligent monitoring of network data to detect anomalies and predict cyber-attacks. This study follows a systematic literature review approach, examining peer-reviewed journal articles and conference proceedings from 2019 to 2024. The analysis shows while blockchain, ML and DL technologies play a crucial role in enhancing IoT security, they each have limitations including scalability, computational overhead, data dependency and lack of flexibility against new cyber threats. Additionally, the majority of studies concentrate on either blockchain-based security or ML-based intrusion detection, with limited study on the integration of the two for real-time threat detection and mitigation. The study highlights this limitation and calls for the development of intelligent hybrid models that integrate blockchain technology with ML/DL to address scalability, adaptability and real-time security for IoT networks to ensure confidentiality, integrity and reliability.
Shamsudeen Mohammed S, Nwobodo-Nzeribe Nnenna Harmony, Aghaizu Herman Chijioke· International journal of re...· 0 citations
BELS-IoT is proposed, a novel decentralized protection architecture that integrates a cryptocurrency-based blockchain layer with a multi-layer ensemble learning engine that rewards honest behavior and penalizes malicious activities while maintaining privacy through federated learning with blockchain-verified reputation scores.
Anwar Kalghoum, Leila Azouz Saidane· SN Computer Science· 0 citations
A distributed intrusion detection framework that integrates blockchain technology with Multi-Agent Reinforcement Learning (MARL) for enhanced blockchain security, transparency, and decentralization and establishes an emerging practice of intelligent distributed intrusion detection in emerging cybersecurity architectures.
Mohammed Zakariah, Fatma S. Alrayes, Mohammed K. Alzaylaee et al.· Cluster Computing· 0 citations
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.
M. Namratha, Kunwar Singh· International Journal of Com...· 0 citations
Results indicate that decentralized, interoperable, and energy-aware intrusion detection is feasible for large-scale IoT deployments, particularly in resource-constrained IoT environments.
S. Bassey, Emmanuel Udoh, B. Stephen et al.· E3S Web of Conferences· 0 citations