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
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1034-1040

TRGATLog:log anomaly detection method based on log time relation graph attention network

Chen Xua
Zhang Shuoa
Jing Yongjuna
Wang Shuyangb
a. School of Computer Science & Engineering, b. School of Electrical & Information Engineering, North Minzu University, Yinchuan 750000, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0365

Abstract

In order to solve the problem that the existing log anomaly detection methods tend to focus only on the single feature of the quantitative relationship mode or the sequential mode, ignoring the relationship of the log time structure and the interrelation between different features, resulting in a high error detection rate and false positive rate, this paper proposed a log anomaly detection method based on the log time graph attention network. Firstly, this paper constructed a log time graph by designing a joint feature extraction module of log semantics and time structure, which effectively integrated the time structure relationship and semantic information of log. Secondly, it constructed the time relationship graph attention network, and used the graph structure to describe the time structure relationship between logs, which could adaptively learn the importance of different logs and carry out anomaly detection. Finally, it used three public datasets to verify the effectiveness of the model. Extensive experiments results indicate that the proposed method is able to effectively capture the temporal structure relationships in the logs, thereby improving the accuracy of anomaly detection.

Foundation Support

中央高校基本科研业务费专项资金资助项目(2022PT_S04)
宁夏回族自治区重点研发项目(2023BDE02017)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0365
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Algorithm Research & Explore
Pages: 1034-1040
Serial Number: 1001-3695(2024)04-011-1034-07

Publish History

[2023-11-02] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

陈旭, 张硕, 景永俊, 等. TRGATLog：基于日志时间图注意力网络的日志异常检测方法 [J]. 计算机应用研究, 2024, 41 (4): 1034-1040. (Chen Xu, Zhang Shuo, Jing Yongjun, et al. TRGATLog:log anomaly detection method based on log time relation graph attention network [J]. Application Research of Computers, 2024, 41 (4): 1034-1040. )

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)