Blockchain based federated learning for intrusion detection for Internet of Things

doi:10.1007/s11704-023-3026-8

Front. Comput. Sci.

2024, Vol. 18

Issue (5) : 185328 https://doi.org/10.1007/s11704-023-3026-8

RESEARCH ARTICLE

Blockchain based federated learning for intrusion detection for Internet of Things

Nan SUN¹, Wei WANG^2,³, Yongxin TONG⁴, Kexin LIU^2,³(

)

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

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Abstract

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.

Keywords intrusion detection federated learning new attacks discovering blockchain

Corresponding Author(s): Kexin LIU

Just Accepted Date: 05 June 2023 Issue Date: 14 September 2023

Cite this article:

Nan SUN,Wei WANG,Yongxin TONG, et al. Blockchain based federated learning for intrusion detection for Internet of Things[J]. Front. Comput. Sci., 2024, 18(5): 185328.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-3026-8
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I5/185328

Fig.1 Distributed federated learning model

Fig.2 Blockchain based federated learning model

Fig.3 Novel attack intrusion detection model

Tab.1 Description of AWID datasets

Method(AWID-CLS)	Accuracy	Flooding	Injection	Impersonation	Normal	Method(AWID-ATK)	Accuracy
J48 [22]	96.2	99.83	100	70.55	96.14	local $σ$ K-means [27]	86.67
Random forest [22]	95.6	99.97	99.92	95.44	96.14	LSTM+DTW [28]	94.41
Majority voting [22]	96.32	100	100	100	96.15	DEKM [29]	95.07
Neural network [22]	99.3	?	?	?	?	ELSC [30]	90.77
Stacked autoencoder [22]	99.88	?	?	?	?	refined ASC [27]	96.63
Autoencoded DNN [22]	99.9	99.42	99.87	99.9	99.93	Federated K-means Cluster [31]	94.8
Proposed	99.74	99.68	100	98.35	99.95	Proposed	98.8

Tab.2 Comparison results on AWID dataset

Fig.4 Comparison results of different algorithms

Tab.3 Experiments results on AWID-ATK dataset

Fig.5 Novel attacks discovering results. (a) Entity 4; (b) Entity 7

Fig.6 Attack detection performance on different training schemes (a) AWID-CLS dataset; (b) AWID-ATK dataset

Fig.7 Running time of different datasets

Fig.8 Comparison results for AWID dataset with and without blockchain

Fig.9 Blockchain verification results for AWID dataset (attack level is 0.9). (a) Proof of Stake for AWID-CLS; (b) Proof of Stake for AWID-ATK

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