App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 7

Semi-supervised log anomaly detection based on bidirectional temporal convolution network

Yin Chunyong
Kong Xian
School of Computer, Nanjing University of Information Science & Technology, Nanjing 210044, China
DOI: 10.19734/j.issn.1001-3695.2023.10.0507

Abstract

Because the accuracy of log parsing is not high and the lack of tag samples reduces the accuracy of anomaly detection, this paper proposes a new semi-supervised anomaly detection method based on logs. First, the method enhances the log parsing method of the dictionary to retain parameter information in log events, improving the utilization and accuracy of log resolution. Next, the method utilizes BERT to encode semantic information in the template, obtaining the semantic vector of the log. Then, the method employs the clustering method to estimate the tag, which effectively alleviates the problem of insufficient data labeling and enhances the model’s ability of detecting unstable data. Finally, the method captures context information from two directions based on the bidirectional temporal convolution network (Bi-TCN) with residual blocks, which enhances the accuracy and efficiency of anomaly detection. To evaluate the method’s performance, extensive experiments are conducted on two datasets. The results demonstrate that the proposed method achieves an average improvement of 7%, 14.1%, and 8.04% in F1 value compared to the latest three benchmark models, LogBERT, PLELog, and LogEncoder, enabling efficient and accurate log parsing and log anomaly detection.

Foundation Support

国家自然科学基金面上项目(6177282)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.10.0507
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 7

Publish History

[2023-12-29] Accepted Paper

Cite This Article

尹春勇, 孔娴. 基于双向时间卷积网络的半监督日志异常检测 [J]. 计算机应用研究, 2024, 41 (7). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.10.0507. (Yin Chunyong, Kong Xian. Semi-supervised log anomaly detection based on bidirectional temporal convolution network [J]. Application Research of Computers, 2024, 41 (7). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.10.0507. )

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.

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