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Technology of Information Security
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 11
3471-3476

Privacy-preserving intrusion detection scheme based on hierarchical K-asynchronous federated learning

Chen Liduo
Wen Mi
Zhang Yanbo
College of Computer Science & Technology, Shanghai University of Electric Power, Shanghai 200090, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0642

Abstract

The widespread application of 5G led to a surge in Internet of Things(IoT) devices and traffic, reducing the efficiency and reliability of IoT intrusion detection systems. Current intrusion detection systems primarily used synchronous distri-buted deep learning methods, which were difficult to apply to real-world distributed asynchronous scenarios. In addition, distributed training processes may suffer from inference attacks. To address these issues, this paper proposed a privacy-preserving intrusion detection scheme based on hierarchical K-asynchronous federated learning. By utilizing corresponding algorithms at different stages of model training, it improved the convergence and accuracy of asynchronous intrusion detection model training. Additionally, this paper designed a gradient masking algorithm to prevent inference attacks during system asynchronous training. Experimental results show that in strongly heterogeneous scenarios, this scheme can increase the accuracy of training on two intrusion detection datasets by 11.8% and 9.8% respectively, offering an effective solution to enhance the efficiency, reliability, and security of intrusion detection systems in IoT environments.

Foundation Support

国家自然科学基金委联合基金资助项目(U23B2021)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0642
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 11
Section: Technology of Information Security
Pages: 3471-3476
Serial Number: 1001-3695(2024)11-039-3471-06

Publish History

[2024-07-10] Accepted Paper
[2024-11-05] Printed Article

Cite This Article

陈力夺, 温蜜, 张研博. 一种基于两级K-异步联邦学习的隐私保护入侵检测方案 [J]. 计算机应用研究, 2024, 41 (11): 3471-3476. (Chen Liduo, Wen Mi, Zhang Yanbo. Privacy-preserving intrusion detection scheme based on hierarchical K-asynchronous federated learning [J]. Application Research of Computers, 2024, 41 (11): 3471-3476. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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