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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Cross-organizational business process anomaly detection based on GRU and Transformer

Yang Fan1
Fang Na1,2
1. School of Mathematics & Big Data, Anhui University of Science & Technology, Huainan Anhui 232001, China
2. Anhui Province Engineering Laboratort for Big Data Analysis & Early Warning Technology of Coal Mine Safety, Huainan Anhui 232001, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0117

Abstract

Cross-organizational processes are critical for modern business collaboration, yet existing anomaly detection methods focus mainly on single-organization views, failing to capture inter-organizational message deviations, contextual mismatches, and global temporal shifts. To address these issues, propose CoBPAD (Cross-organizational Business Processes Anomaly Detection) , a model that integrates GRU for capturing temporal dependencies with a Transformer to model cross-organizational interaction patterns. Apply teacher forcing during training to improve learning efficiency. CoBPAD identifies anomalies from three perspectives: point anomalies via behavioral deviations, contextual anomalies through rule matching, and collective anomalies by analyzing temporal structure changes. Experiments on datasets from three domains demonstrate that CoBPAD outperforms the representative method BAnDIT in detecting various types of anomalies. The results show that CoBPAD improves detection capability and adaptability in complex collaboration environments and provides a solid foundation for future anomaly explanation and real-time monitoring.

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资助项目(2022a05030005)
安徽省自然科学基金资助项目(水科学联合基金2308085US11)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0117
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-17] Accepted Paper

Cite This Article

杨凡, 方娜. 基于GRU和Transformer的跨组织业务流程异常检测 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0117. (Yang Fan, Fang Na. Cross-organizational business process anomaly detection based on GRU and Transformer [J]. Application Research of Computers, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0117. )

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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