Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey
The Internet of Things (IoT) is revolutionizing industries by enabling seamless interconnectivity across domains such as healthcare, smart cities, the Industrial Internet of Things (IIoT), and the Internet of Vehicles (IoV). However, IoT security remains a significant challenge due to vulnerabilitie...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3341 |
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| author | Abdullah Alfahaid Easa Alalwany Abdulqader M. Almars Fatemah Alharbi Elsayed Atlam Imad Mahgoub |
| author_facet | Abdullah Alfahaid Easa Alalwany Abdulqader M. Almars Fatemah Alharbi Elsayed Atlam Imad Mahgoub |
| author_sort | Abdullah Alfahaid |
| collection | DOAJ |
| description | The Internet of Things (IoT) is revolutionizing industries by enabling seamless interconnectivity across domains such as healthcare, smart cities, the Industrial Internet of Things (IIoT), and the Internet of Vehicles (IoV). However, IoT security remains a significant challenge due to vulnerabilities related to data breaches, privacy concerns, cyber threats, and trust management issues. Addressing these risks requires advanced security mechanisms, with machine learning (ML) emerging as a powerful tool for anomaly detection, intrusion detection, and threat mitigation. This survey provides a comprehensive review of ML-driven IoT security solutions from 2020 to 2024, examining the effectiveness of supervised, unsupervised, and reinforcement learning approaches, as well as advanced techniques such as deep learning (DL), ensemble learning (EL), federated learning (FL), and transfer learning (TL). A systematic classification of ML techniques is presented based on their IoT security applications, along with a taxonomy of security threats and a critical evaluation of existing solutions in terms of scalability, computational efficiency, and privacy preservation. Additionally, this study identifies key limitations of current ML approaches, including high computational costs, adversarial vulnerabilities, and interpretability challenges, while outlining future research opportunities such as privacy-preserving ML, explainable AI, and edge-based security frameworks. By synthesizing insights from recent advancements, this paper provides a structured framework for developing robust, intelligent, and adaptive IoT security solutions. The findings aim to guide researchers and practitioners in designing next-generation cybersecurity models capable of effectively countering emerging threats in IoT ecosystems. |
| format | Article |
| id | doaj-art-78904d60bc984310a12a5f2cb04e944d |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-78904d60bc984310a12a5f2cb04e944d2025-08-20T02:23:09ZengMDPI AGSensors1424-82202025-05-012511334110.3390/s25113341Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive SurveyAbdullah Alfahaid0Easa Alalwany1Abdulqader M. Almars2Fatemah Alharbi3Elsayed Atlam4Imad Mahgoub5Department of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi ArabiaDepartment of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USAThe Internet of Things (IoT) is revolutionizing industries by enabling seamless interconnectivity across domains such as healthcare, smart cities, the Industrial Internet of Things (IIoT), and the Internet of Vehicles (IoV). However, IoT security remains a significant challenge due to vulnerabilities related to data breaches, privacy concerns, cyber threats, and trust management issues. Addressing these risks requires advanced security mechanisms, with machine learning (ML) emerging as a powerful tool for anomaly detection, intrusion detection, and threat mitigation. This survey provides a comprehensive review of ML-driven IoT security solutions from 2020 to 2024, examining the effectiveness of supervised, unsupervised, and reinforcement learning approaches, as well as advanced techniques such as deep learning (DL), ensemble learning (EL), federated learning (FL), and transfer learning (TL). A systematic classification of ML techniques is presented based on their IoT security applications, along with a taxonomy of security threats and a critical evaluation of existing solutions in terms of scalability, computational efficiency, and privacy preservation. Additionally, this study identifies key limitations of current ML approaches, including high computational costs, adversarial vulnerabilities, and interpretability challenges, while outlining future research opportunities such as privacy-preserving ML, explainable AI, and edge-based security frameworks. By synthesizing insights from recent advancements, this paper provides a structured framework for developing robust, intelligent, and adaptive IoT security solutions. The findings aim to guide researchers and practitioners in designing next-generation cybersecurity models capable of effectively countering emerging threats in IoT ecosystems.https://www.mdpi.com/1424-8220/25/11/3341internet of things (IoT)IoT securitycybersecuritymachine learning (ML)anomaly detectionintrusion detection systems (IDSs) |
| spellingShingle | Abdullah Alfahaid Easa Alalwany Abdulqader M. Almars Fatemah Alharbi Elsayed Atlam Imad Mahgoub Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey Sensors internet of things (IoT) IoT security cybersecurity machine learning (ML) anomaly detection intrusion detection systems (IDSs) |
| title | Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey |
| title_full | Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey |
| title_fullStr | Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey |
| title_full_unstemmed | Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey |
| title_short | Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey |
| title_sort | machine learning based security solutions for iot networks a comprehensive survey |
| topic | internet of things (IoT) IoT security cybersecurity machine learning (ML) anomaly detection intrusion detection systems (IDSs) |
| url | https://www.mdpi.com/1424-8220/25/11/3341 |
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