A survey on intrusion detection system in IoT networks
As the Internet of Things (IoT) expands, the security of IoT networks has becoming more critical. Intrusion Detection Systems (IDS) are essential for protecting these networks against malicious activities. Artificial intelligence, with its adaptive and self-learning capabilities, has emerged as a pr...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-12-01
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| Series: | Cyber Security and Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772918424000481 |
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| author | Md Mahbubur Rahman Shaharia Al Shakil Mizanur Rahman Mustakim |
| author_facet | Md Mahbubur Rahman Shaharia Al Shakil Mizanur Rahman Mustakim |
| author_sort | Md Mahbubur Rahman |
| collection | DOAJ |
| description | As the Internet of Things (IoT) expands, the security of IoT networks has becoming more critical. Intrusion Detection Systems (IDS) are essential for protecting these networks against malicious activities. Artificial intelligence, with its adaptive and self-learning capabilities, has emerged as a promising approach to enhancing intrusion detection in IoT environments. Machine learning facilitates dynamic threat identification, reduces false positives, and addresses evolving vulnerabilities. This survey provides an analysis of contemporary intrusion detection techniques, models, and their performances in IoT networks, offering insights into IDS design and implementation. It reviews data extraction techniques, useful matrices, and loss functions in IDS for IoT networks, ranking top-cited algorithms and categorizing IDS studies based on different approaches. The survey evaluates various datasets used in IoT intrusion detection, examining their attributes, benefits, and drawbacks, and emphasizes performance metrics and computational efficiency, providing insights into IDS effectiveness and practicality. Standardized evaluation metrics and real-world testing are stressed to ensure reliability. Additionally, the survey identifies significant challenges and open issues in ML and DL-based IDS for IoT networks, such as computational complexity and high false positive rates, and recommends potential research directions, emerging trends, and perspectives for future work. This forward-looking perspective aids in shaping the future direction of research in this dynamic field, emphasizing the need for lightweight, efficient IDS models suitable for resource- constrained IoT devices and the importance of comprehensive, representative datasets. |
| format | Article |
| id | doaj-art-d76477096dea49d49342eb3f4a98cb1b |
| institution | Kabale University |
| issn | 2772-9184 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Cyber Security and Applications |
| spelling | doaj-art-d76477096dea49d49342eb3f4a98cb1b2025-01-07T04:17:38ZengKeAi Communications Co., Ltd.Cyber Security and Applications2772-91842025-12-013100082A survey on intrusion detection system in IoT networksMd Mahbubur Rahman0Shaharia Al Shakil1Mizanur Rahman Mustakim2Comp. Sci. & Tech., Beijing Institute of Technology, Beijing, ChinaCorresponding author.; Info. & Comm. Eng., Beijing Institute of Technology, Beijing, ChinaComp. Sci. & Tech., Beijing Institute of Technology, Beijing, ChinaAs the Internet of Things (IoT) expands, the security of IoT networks has becoming more critical. Intrusion Detection Systems (IDS) are essential for protecting these networks against malicious activities. Artificial intelligence, with its adaptive and self-learning capabilities, has emerged as a promising approach to enhancing intrusion detection in IoT environments. Machine learning facilitates dynamic threat identification, reduces false positives, and addresses evolving vulnerabilities. This survey provides an analysis of contemporary intrusion detection techniques, models, and their performances in IoT networks, offering insights into IDS design and implementation. It reviews data extraction techniques, useful matrices, and loss functions in IDS for IoT networks, ranking top-cited algorithms and categorizing IDS studies based on different approaches. The survey evaluates various datasets used in IoT intrusion detection, examining their attributes, benefits, and drawbacks, and emphasizes performance metrics and computational efficiency, providing insights into IDS effectiveness and practicality. Standardized evaluation metrics and real-world testing are stressed to ensure reliability. Additionally, the survey identifies significant challenges and open issues in ML and DL-based IDS for IoT networks, such as computational complexity and high false positive rates, and recommends potential research directions, emerging trends, and perspectives for future work. This forward-looking perspective aids in shaping the future direction of research in this dynamic field, emphasizing the need for lightweight, efficient IDS models suitable for resource- constrained IoT devices and the importance of comprehensive, representative datasets.http://www.sciencedirect.com/science/article/pii/S2772918424000481Cyber-physical systemsDistributed denial-of-serviceInternet of ThingsIntrusion detectionMachine learning |
| spellingShingle | Md Mahbubur Rahman Shaharia Al Shakil Mizanur Rahman Mustakim A survey on intrusion detection system in IoT networks Cyber Security and Applications Cyber-physical systems Distributed denial-of-service Internet of Things Intrusion detection Machine learning |
| title | A survey on intrusion detection system in IoT networks |
| title_full | A survey on intrusion detection system in IoT networks |
| title_fullStr | A survey on intrusion detection system in IoT networks |
| title_full_unstemmed | A survey on intrusion detection system in IoT networks |
| title_short | A survey on intrusion detection system in IoT networks |
| title_sort | survey on intrusion detection system in iot networks |
| topic | Cyber-physical systems Distributed denial-of-service Internet of Things Intrusion detection Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772918424000481 |
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