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Blockchain based federated learning for intrusion detection for Internet of Things

SUN, Nan ; WANG, Wei ; TONG, Yongxin ; LIU, Kexin

Frontiers of Computer Science, 2024-10, Vol.18 (5), p.185328, Article 185328 [Periódico revisado por pares]

Beijing: Higher Education Press

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Título:
Blockchain based federated learning for intrusion detection for Internet of Things
Autor: SUN, Nan ; WANG, Wei ; TONG, Yongxin ; LIU, Kexin
Assuntos: Accuracy ; Algorithms ; Blockchain ; Classification ; Communication ; Computer engineering ; Computer Science ; Cryptography ; Cybersecurity ; Datasets ; Federated learning ; Internet of Things ; intrusion detection ; Intrusion detection systems ; Network topologies ; new attacks discovering ; Privacy ; Research Article
É parte de: Frontiers of Computer Science, 2024-10, Vol.18 (5), p.185328, Article 185328
Notas: Document received on :2023-01-11
intrusion detection
new attacks discovering
blockchain
Document accepted on :2023-06-01
federated learning
Descrição: In Internet of Things (IoT), data sharing among different devices can improve manufacture efficiency and reduce workload, and yet make the network systems be more vulnerable to various intrusion attacks. There has been realistic demand to develop an efficient intrusion detection algorithm for connected devices. Most of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack types. In this paper, a distributed federated intrusion detection method is proposed, utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack types. Besides, the blockchain technique is introduced in the federated learning process for the consensus of the entire framework. Experimental results are provided to show that our approach can identify the malicious entities, while outperforming the existing methods in discovering new intrusion attack types.
Editor: Beijing: Higher Education Press
Idioma: Inglês

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Blockchain based federated learning for intrusion detection for Internet of Things

SUN, Nan ; WANG, Wei ; TONG, Yongxin ; LIU, Kexin

Beijing: Higher Education Press

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