Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review
Abstract As the Internet of Things (IoT) grows, ensuring robust security is crucial. Intrusion Detection Systems (IDS) protect IoT networks from various cyber threats. This systematic literature review (SLR) explores the advancements in training and deployment strategies of Artificial Intelligence (...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Discover Internet of Things |
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| Online Access: | https://doi.org/10.1007/s43926-025-00099-4 |
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| _version_ | 1832585418114072576 |
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| author | S. Kumar Reddy Mallidi Rajeswara Rao Ramisetty |
| author_facet | S. Kumar Reddy Mallidi Rajeswara Rao Ramisetty |
| author_sort | S. Kumar Reddy Mallidi |
| collection | DOAJ |
| description | Abstract As the Internet of Things (IoT) grows, ensuring robust security is crucial. Intrusion Detection Systems (IDS) protect IoT networks from various cyber threats. This systematic literature review (SLR) explores the advancements in training and deployment strategies of Artificial Intelligence (AI) based IDS within IoT environments. The study begins by outlining prevalent IoT attacks and developing an updated taxonomy to enhance understanding of these threats. It then examines various IDS architectures and delves into the integration of machine learning (ML) and deep learning (DL) technologies that improve detection capabilities and system responsiveness. Significant emphasis is placed on analyzing different IDS training paradigms-centralized, distributed, and federated learning-and deployment strategies, including in cloud, fog, and edge layers. Their effectiveness within IoT contexts is evaluated comprehensively. Moreover, the review assesses the datasets commonly used for training and validating IDS and discusses key performance metrics and validation measures critical for assessing IDS effectiveness. The study concludes by synthesizing the major challenges facing current IDS implementations in IoT and suggesting future research directions aimed at overcoming these hurdles. This review highlights the technological advancements in IDS and sets the stage for future enhancements in IoT security, emphasizing the integration of innovative training and deployment strategies. |
| format | Article |
| id | doaj-art-474f4f0037084702a7b53650825cf31e |
| institution | Kabale University |
| issn | 2730-7239 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Internet of Things |
| spelling | doaj-art-474f4f0037084702a7b53650825cf31e2025-01-26T12:48:31ZengSpringerDiscover Internet of Things2730-72392025-01-015113310.1007/s43926-025-00099-4Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature reviewS. Kumar Reddy Mallidi0Rajeswara Rao Ramisetty1Computer Science and Engineering, Jawaharlal Nehru Technological UniversityComputer Science and Engineering, Jawaharlal Nehru Technological University GurajadaAbstract As the Internet of Things (IoT) grows, ensuring robust security is crucial. Intrusion Detection Systems (IDS) protect IoT networks from various cyber threats. This systematic literature review (SLR) explores the advancements in training and deployment strategies of Artificial Intelligence (AI) based IDS within IoT environments. The study begins by outlining prevalent IoT attacks and developing an updated taxonomy to enhance understanding of these threats. It then examines various IDS architectures and delves into the integration of machine learning (ML) and deep learning (DL) technologies that improve detection capabilities and system responsiveness. Significant emphasis is placed on analyzing different IDS training paradigms-centralized, distributed, and federated learning-and deployment strategies, including in cloud, fog, and edge layers. Their effectiveness within IoT contexts is evaluated comprehensively. Moreover, the review assesses the datasets commonly used for training and validating IDS and discusses key performance metrics and validation measures critical for assessing IDS effectiveness. The study concludes by synthesizing the major challenges facing current IDS implementations in IoT and suggesting future research directions aimed at overcoming these hurdles. This review highlights the technological advancements in IDS and sets the stage for future enhancements in IoT security, emphasizing the integration of innovative training and deployment strategies.https://doi.org/10.1007/s43926-025-00099-4IoTIDSDeployment strategiesTraining paradigmsMachine learningFederated learning |
| spellingShingle | S. Kumar Reddy Mallidi Rajeswara Rao Ramisetty Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review Discover Internet of Things IoT IDS Deployment strategies Training paradigms Machine learning Federated learning |
| title | Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review |
| title_full | Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review |
| title_fullStr | Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review |
| title_full_unstemmed | Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review |
| title_short | Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review |
| title_sort | advancements in training and deployment strategies for ai based intrusion detection systems in iot a systematic literature review |
| topic | IoT IDS Deployment strategies Training paradigms Machine learning Federated learning |
| url | https://doi.org/10.1007/s43926-025-00099-4 |
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