Blockchain based federated learning for intrusion detection for Internet of Things
Nan SUN1, Wei WANG2,3, Yongxin TONG4, Kexin LIU2,3()
1. School of Cyber Science and Technology, Beihang University, Beijing 100191, China 2. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China 3. Zhongguancun Laboratory, Beijing 100094, China 4. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
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.
. [J]. Frontiers of Computer Science, 2024, 18(5): 185328.
Nan SUN, Wei WANG, Yongxin TONG, Kexin LIU. Blockchain based federated learning for intrusion detection for Internet of Things. Front. Comput. Sci., 2024, 18(5): 185328.
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