In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Technology of Information Security
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 3
829-832

Intrusion detection algorithm based on depth sequence weighted kernel extreme learning

Wang Yang
Wu Zhongdong
Zhu Jing
School of Electronic & Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
DOI: 10.19734/j.issn.1001-3695.2018.08.0653

Abstract

This paper proposed an intrusion detection algorithm based on deep sequence weighting kernel limit learning(DBN-WOS-KELM) to solve the problem of massive multi-source heterogeneous network intrusion detection with unbalanced data distribution and the problem, that the traditional deep learning algorithm could not update its output weight online according to the real-time intrusion situation. The algorithm firstly used the deep belief network DBN to study the historical data, then extracted the features of the original data and reduced the dimension of the data. And then it used the weighted sequence kernel extreme learning machine for supervised learning to complete the intrusion detection. It combined the ability of extracting abstract features from the deep belief network and the fast learning ability of the kernel extreme learning machine. Finally, the simulation experiments on KDD99 dataset show that DBN-WOS-KELM algorithm improves the recognition rate of small sample attacks, and can update the output weights online according to the real-time situation, so that the training efficiency is much higher.

Foundation Support

甘肃省高等学校创新团队项目(2017C-09)
中国铁路总公司科技研究开发计划重大课题(2017X013-A)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.08.0653
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 3
Section: Technology of Information Security
Pages: 829-832
Serial Number: 1001-3695(2020)03-041-0829-04

Publish History

[2020-03-05] Printed Article

Cite This Article

汪洋, 伍忠东, 朱婧. 基于深度序列加权核极限学习的入侵检测算法 [J]. 计算机应用研究, 2020, 37 (3): 829-832. (Wang Yang, Wu Zhongdong, Zhu Jing. Intrusion detection algorithm based on depth sequence weighted kernel extreme learning [J]. Application Research of Computers, 2020, 37 (3): 829-832. )

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)