Network intrusion detection algorithm combining blockchain and federated learning

Dongyang Cai; Kaili Zhao; Jie Bai; Rui Bo; Xuening Zhang; Guopeng Zhao; Yongmin Cao

doi:10.1117/12.3034788

11 July 2024 Network intrusion detection algorithm combining blockchain and federated learning

Dongyang Cai, Kaili Zhao, Jie Bai, Rui Bo, Xuening Zhang, Guopeng Zhao, Yongmin Cao

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132101B (2024) https://doi.org/10.1117/12.3034788
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

With the development of network security technology, the situation of network intrusion is becoming increasingly serious. Traditional network intrusion detection methods have the problem of low detection efficiency and the lack of encryption processing in detection rules, which may be cracked by attackers. In order to improve the intrusion detection ability of frequent data transmission in the network, In this paper, a federated learning method based on blockchain technology is proposed, which uses blockchain technology, chain data structure and data block rules to verify the relevant information in passwords, and inserts password strings in multiple blocks to improve the intrusion detection ability of network information. At the same time, this paper analyzes the reasons of information theft and interference, summarizes the characteristics of network intrusion, puts forward the design idea of integrating blockchain technology, and verifies the correctness of this processing method, the fitting of network data and the degree of intrusion detection through actual cases. MATLAB simulation results show that the federated learning method has better network intrusion ability, the intrusion detection degree is over 90%, and the intrusion detection ability rises first and then declines, the overall index is better than the online monitoring method. Therefore, the fusion of blockchain and federated learning method proposed in this paper is suitable for the optimization of network intrusion monitoring. There are still some shortcomings in this research, mainly because there are few research materials related to blockchain technology, which leads to less research data in this paper, and more data will be collected for analysis in the future.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dongyang Cai, Kaili Zhao, Jie Bai, Rui Bo, Xuening Zhang, Guopeng Zhao, and Yongmin Cao "Network intrusion detection algorithm combining blockchain and federated learning", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132101B (11 July 2024); https://doi.org/10.1117/12.3034788

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KEYWORDS

Blockchain

Computer intrusion detection

Data transmission

Computer security

Data modeling

Data processing

Network security

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