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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 1

Business process anomaly detection method based on siamese transformer

Pan Kexua
Fang Naa,b
a. School of Mathematics & Big Data, b. Anhui Province Engineering Laboratory for Big Data Analysis & Early Warning Technology of Coal Mine Safety, Anhui University of Science & Technology, Huainan Anhui 232001, China
DOI: 10.19734/j.issn.1001-3695.2025.06.0208

Abstract

Business process anomaly detection is essential for risk management and process optimization. The dynamic complexity of business processes makes abnormal pattern recognition in event logs challenging. Existing unsupervised methods cannot effectively capture deep semantic relationships between events and are vulnerable to noise during training. This paper proposed a few-shot anomaly detection method based on a Siamese Transformer that integrates semantic and sequential information. The method used the Local Outlier Factor algorithm to extract a small set of high-confidence normal and abnormal samples, establishing contrastive relationships to reveal semantic differences. A sampling strategy then constructed training sample pairs and screened anchor sample sets. These pairs were fed into a Siamese Transformer encoder to capture sequential patterns and learn behavioral similarities among process instances. The pre-trained model quantified the similarity between test samples and the anchor set. Samples that exceeded a predefined threshold are identified as anomalous. Experimental results on multiple public real-world event log datasets show that the method achieves higher F1-scores in case-level anomaly detection than baseline approaches. Additional experiments with incremental noise confirm that the method is robust against data contamination.

Foundation Support

国家自然科学基金资助项目61572035,安徽省重点研究与开发计划项目(2022a05020005)
安徽省自然科学基金项目(水科学联合基金,2308085US11)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.06.0208
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 1

Publish History

[2025-09-17] Accepted Paper

Cite This Article

潘克旭, 方娜. 基于孪生Transformer的业务流程异常检测方法 [J]. 计算机应用研究, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0208. (Pan Kexu, Fang Na. Business process anomaly detection method based on siamese transformer [J]. Application Research of Computers, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0208. )

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.

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