A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoT
In the era of pervasive connectivity, the widespread deployment of Internet of Things (IoT) devices across various applications has led to a rise in malicious attacks, necessitating the development of robust network intrusion detection systems (IDS) for IoT. Traditional deep learning-based IDS are c...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2025.2518963 |
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| author | Li Ma Jicheng He Kai Lu Dan Wang Long Yin Zhaokun Li |
| author_facet | Li Ma Jicheng He Kai Lu Dan Wang Long Yin Zhaokun Li |
| author_sort | Li Ma |
| collection | DOAJ |
| description | In the era of pervasive connectivity, the widespread deployment of Internet of Things (IoT) devices across various applications has led to a rise in malicious attacks, necessitating the development of robust network intrusion detection systems (IDS) for IoT. Traditional deep learning-based IDS are challenged by the diversity of IoT devices, resource constraints, and the non-identically and independently distributed (non-IID) nature of IoT data. To address these issues, this paper introduces two solutions: CF-IDS and KF-IDS. CF-IDS is a federated learning-based IDS designed for cloud environments, preserving data privacy and addressing non-IID challenges. It utilizes contrastive learning and clustering to create pseudo-labels for public datasets, improving cold-start performance by aligning client models in the early training process. KF-IDS, as an additional (but optional) subsequent step of CF-IDS, based on knowledge distillation and mutual information theory, provides a off-line IDS solution by distillating CF-IDS into lite models. Through local deployment on low-computational-power end devices, KF-IDS avoiding upload raw data to the cloud. Experimental evaluations on public IoT datasets demonstrate that CF-IDS achieves 80.71% accuracy in non-IID scenarios, outperforming other federated learning methods such as FedNova. KF-IDS reduces time consumption by 46.81% and memory usage by 48.30% than CF-IDS while maintaining accuracy, which emphasizes the low cost of KF-IDS, such as deployed on affordable NVIDIA Jetson Nano rather than expensive RTX 4090 or Tesla A100. |
| format | Article |
| id | doaj-art-6ce5ca1669194ee29af5cb5dc0acc54d |
| institution | OA Journals |
| issn | 2164-2583 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-6ce5ca1669194ee29af5cb5dc0acc54d2025-08-20T02:07:27ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832025-12-0113110.1080/21642583.2025.2518963A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoTLi Ma0Jicheng He1Kai Lu2Dan Wang3Long Yin4Zhaokun Li5Beijing Kedong Electric Power Control System Co.Ltd., Beijing, People's Republic of ChinaBeijing Kedong Electric Power Control System Co.Ltd., Beijing, People's Republic of ChinaBeijing Kedong Electric Power Control System Co.Ltd., Beijing, People's Republic of ChinaBeijing Kedong Electric Power Control System Co.Ltd., Beijing, People's Republic of ChinaSoftware College, Northeastern University, Shenyang, People's Republic of ChinaSoftware College, Northeastern University, Shenyang, People's Republic of ChinaIn the era of pervasive connectivity, the widespread deployment of Internet of Things (IoT) devices across various applications has led to a rise in malicious attacks, necessitating the development of robust network intrusion detection systems (IDS) for IoT. Traditional deep learning-based IDS are challenged by the diversity of IoT devices, resource constraints, and the non-identically and independently distributed (non-IID) nature of IoT data. To address these issues, this paper introduces two solutions: CF-IDS and KF-IDS. CF-IDS is a federated learning-based IDS designed for cloud environments, preserving data privacy and addressing non-IID challenges. It utilizes contrastive learning and clustering to create pseudo-labels for public datasets, improving cold-start performance by aligning client models in the early training process. KF-IDS, as an additional (but optional) subsequent step of CF-IDS, based on knowledge distillation and mutual information theory, provides a off-line IDS solution by distillating CF-IDS into lite models. Through local deployment on low-computational-power end devices, KF-IDS avoiding upload raw data to the cloud. Experimental evaluations on public IoT datasets demonstrate that CF-IDS achieves 80.71% accuracy in non-IID scenarios, outperforming other federated learning methods such as FedNova. KF-IDS reduces time consumption by 46.81% and memory usage by 48.30% than CF-IDS while maintaining accuracy, which emphasizes the low cost of KF-IDS, such as deployed on affordable NVIDIA Jetson Nano rather than expensive RTX 4090 or Tesla A100.https://www.tandfonline.com/doi/10.1080/21642583.2025.2518963Internet of thingsfederated learningintrusion detectionartificial intelligence |
| spellingShingle | Li Ma Jicheng He Kai Lu Dan Wang Long Yin Zhaokun Li A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoT Systems Science & Control Engineering Internet of things federated learning intrusion detection artificial intelligence |
| title | A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoT |
| title_full | A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoT |
| title_fullStr | A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoT |
| title_full_unstemmed | A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoT |
| title_short | A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoT |
| title_sort | contrastive learning and knowledge distillation based framework for efficient federated intrusion detection in iot |
| topic | Internet of things federated learning intrusion detection artificial intelligence |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2025.2518963 |
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