A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoT

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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Bibliographic Details
Main Authors: Li Ma, Jicheng He, Kai Lu, Dan Wang, Long Yin, Zhaokun Li
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Systems Science & Control Engineering
Subjects:
Internet of things
federated learning
intrusion detection
artificial intelligence
Online Access:https://www.tandfonline.com/doi/10.1080/21642583.2025.2518963
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https://www.tandfonline.com/doi/10.1080/21642583.2025.2518963

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