In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Abnormal traffic detection model for edge collaborative networks based on adaptive federated learning

Chen Tao1
Xue Haoming1
Ma Yuxiang1
Guan Haoqi2
1. School of Computer & Information Engineering, Henan University, Kaifeng 475000, China
2. International School of Technology, Henan University, Zhengzhou 450046, China
DOI: 10.19734/j.issn.1001-3695.2025.03.0096

Abstract

With the development of artificial intelligence, edge-end collaborative networks are increasingly applied in intelligent decision-making and local data processing. However, edge-end collaborative networks face privacy leakage, limited computational resources, and data imbalance. This paper proposed an adaptive federated learning-based anomaly traffic detection model that protects privacy while improving detection performance to address these challenges. First, this paper introduced a variance-based feature selection algorithm to reduce computational overhead. Second, this paper proposed a contrastive generative adversarial network (GAN) to alleviate the data imbalance. Third, this paper designed an adaptive federated learning update strategy, where a conditional policy network automatically separates global and local feature information, enhancing the model's generalization ability on heterogeneous data. Finally, this paper developed a lightweight encoder based on convolutional neural networks (CNNs) to perform anomaly traffic detection. Experimental results showed that the proposed method effectively detects anomaly traffic in edge-end collaborative networks, improving anomaly detection accuracy.

Foundation Support

河南省优秀青年基金资助项目(252300421230)
河南省重点研发专项项目(241111212800)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.03.0096
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-03] Accepted Paper

Cite This Article

陈涛, 薛皓铭, 马宇翔, 等. 基于自适应联邦学习的边端协同网络异常流量检测模型 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-08). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0096. (Chen Tao, Xue Haoming, Ma Yuxiang, et al. Abnormal traffic detection model for edge collaborative networks based on adaptive federated learning [J]. Application Research of Computers, 2025, 42 (11). (2025-07-08). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0096. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)