Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection
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 secur...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10849557/ |
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| author | William Villegas-Ch Jaime Govea Rommel Gutierrez Aracely Mera-Navarrete |
| author_facet | William Villegas-Ch Jaime Govea Rommel Gutierrez Aracely Mera-Navarrete |
| author_sort | William Villegas-Ch |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-36359223d14f46adb31fb1e5b984d3f5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-36359223d14f46adb31fb1e5b984d3f52025-01-31T00:02:02ZengIEEEIEEE Access2169-35362025-01-0113169331695810.1109/ACCESS.2025.353280010849557Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat DetectionWilliam Villegas-Ch0https://orcid.org/0000-0002-5421-7710Jaime Govea1Rommel Gutierrez2Aracely Mera-Navarrete3Escuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, EcuadorEscuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, EcuadorEscuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, EcuadorDepartamento de Sistemas, Universidad Internacional del Ecuador, Quito, EcuadorThe 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.https://ieeexplore.ieee.org/document/10849557/Artificial intelligenceblockchainIoT securityhybrid systemsanomaly detectionenergy efficiency |
| spellingShingle | William Villegas-Ch Jaime Govea Rommel Gutierrez Aracely Mera-Navarrete Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection IEEE Access Artificial intelligence blockchain IoT security hybrid systems anomaly detection energy efficiency |
| title | Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection |
| title_full | Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection |
| title_fullStr | Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection |
| title_full_unstemmed | Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection |
| title_short | Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection |
| title_sort | optimizing security in iot ecosystems using hybrid artificial intelligence and blockchain models a scalable and efficient approach for threat detection |
| topic | Artificial intelligence blockchain IoT security hybrid systems anomaly detection energy efficiency |
| url | https://ieeexplore.ieee.org/document/10849557/ |
| work_keys_str_mv | AT williamvillegasch optimizingsecurityiniotecosystemsusinghybridartificialintelligenceandblockchainmodelsascalableandefficientapproachforthreatdetection AT jaimegovea optimizingsecurityiniotecosystemsusinghybridartificialintelligenceandblockchainmodelsascalableandefficientapproachforthreatdetection AT rommelgutierrez optimizingsecurityiniotecosystemsusinghybridartificialintelligenceandblockchainmodelsascalableandefficientapproachforthreatdetection AT aracelymeranavarrete optimizingsecurityiniotecosystemsusinghybridartificialintelligenceandblockchainmodelsascalableandefficientapproachforthreatdetection |