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.
Technology of Information Security
|
2164-2168,2178

Improved method of optimal fish swarm optimization for industrial control network communication anomaly detection

Chen Wanzhi1
Tang Yu1
Zhang Jing2
1. School of Electronic & Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China
2. China Petroleum Liaohe Equipment Company, Panjin Liaoning 124010, China

Abstract

Aiming at typical attack types of industrial control networks, this paper proposed a method of predicting communication anomalies in industrial networks using deep learning. First, it used the principal component analysis of the raw data reduction and eliminated the correlation between the original data set. Secondly, it built artificial neural networks and to optimize the input weights and threshold limits the use of machine learning. It improved the fish swarm algorithm by the idea of particle inertia mass calculation in the gravitational search algorithm. The test experiment results show that it improves the accuracy of anomaly detection, and effectively shortens the detection time. And it realizes the purpose of making use of the depth learning to predict the abnormal behavior of communication in industrial networks.

Foundation Support

辽宁省教育厅服务地方类项目(LJ2017FAL009)
辽宁工程技术大学博士启动基金资助项目(2015-1147)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.01.0099
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 7
Section: Technology of Information Security
Pages: 2164-2168,2178
Serial Number: 1001-3695(2019)07-052-2164-05

Publish History

[2019-07-05] Printed Article

Cite This Article

陈万志, 唐雨, 张静. 工业控制网络通信异常检测的改进鱼群算法优化方法 [J]. 计算机应用研究, 2019, 36 (7): 2164-2168,2178. (Chen Wanzhi, Tang Yu, Zhang Jing. Improved method of optimal fish swarm optimization for industrial control network communication anomaly detection [J]. Application Research of Computers, 2019, 36 (7): 2164-2168,2178. )

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)