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Title	Optimizing Security In Iot Ecosystems Using Hybrid Artificial Intelligence And Blockchain Models: A Scalable And Efficient Approach For Threat Detection
ID_Doc	40871
Authors	Villegas-Ch W.; Govea J.; Gutierrez R.; Mera-Navarrete A.
Year	2025
Published	IEEE Access, 13
DOI	http://dx.doi.org/10.1109/ACCESS.2025.3532800
Abstract	The exponential growth of the Internet of Things (IoT) has boosted connectivity across various sectors, such as Industry 4.0 and smart cities. However, this expansion has also exposed IoT devices to critical vulnerabilities, including spoofing, DoS attacks, and unauthorized access. Traditional security solutions, based on centralized architectures, are neither scalable nor efficient enough to handle the increasing complexity and number of IoT devices, leading to high latencies, increased energy consumption, and inadequate intrusion detection. In this work, we propose a hybrid solution that combines Blockchain and artificial intelligence (AI) to improve security and operational efficiency in IoT networks. Blockchain ensures device authentication and data integrity through a lightweight consensus protocol, while AI enables real-time intrusion detection using deep learning models. The simulations demonstrate that the proposed system improves the precision of detecting phishing attacks by up to 95.2%. At the same time, the authentication latency is reduced to 15 ms in networks with 1000 connected devices, 66.6% faster than traditional solutions. In addition, the energy consumption of the hybrid system is 31.8% lower than that of conventional approaches, validating its scalability and efficiency in large-scale IoT networks. © 2013 IEEE.
Author Keywords	adaptive learning models; anomaly detection; Artificial intelligence; blockchain; energy efficiency; hybrid systems; IoT security

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