TL;DR
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.
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.
Results indicate that decentralized, interoperable, and energy-aware intrusion detection is feasible for large-scale IoT deployments, particularly in resource-constrained IoT environments.
A Blockchain-Based IoT Security Architecture that integrates distributed ledger technology, smart contracts, edge computing, and zero-trust authentication mechanisms to enhance security, privacy, and system reliability is proposed.
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.
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.
The accelerated growth of IoT-facilitated e-learning ecosystems has introduced significant challenges for secure, scalable, and privacy-aware user authentication. Existing approaches face a fundamental trade-off: conventional blockchain systems often incur high latency and limited scalability, while centralized federated learning architectures may introduce privacy concerns and single points of failure. This study presents an authentication framework that integrates sharded blockchain architecture with federated learning to address these challenges. The proposed framework incorporates shard-based transaction processing, Byzantine fault tolerant (PBFT) consensus, resilient federated aggregation, and AES-GCM-encrypted model updates. Experimental results obtained under the adopted simulation settings indicate authentication accuracy of 94.03%, an AUC of 97.79%, an F1-score of 94.37%, and an EER of 5.97%. Compared with the selected blockchain-based baseline methods, the proposed framework demonstrated higher throughput and lower authentication latency under the evaluated workloads.
