| 引用本文: | 陈国威1 ,卢 可1,2.基于自注意力和 Bi-LSTM 的业务流程异常检测模型(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（2）：112-119 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 业务流程中的一项重要工作是进行数据的异常检测,它可以用于监控和识别企业或组织中出现的异常情
况。 目的 针对目前业务流程异常检测方法大多数只考虑控制流,并未考虑事件日志中其他数据属性对业务流程影
响的情况,提出一个多视角无监督异常检测模型。 方法 首先,将控制流和数据流分别进行处理,然后拼接形成可以
输入到模型中的数据类型;其次,利用自注意力机制和 Bi-LSTM 自编码器组合成的模型,分别对控制流视角和数
据流视角进行业务流程事件日志的特征提取,并进行拼接和异常检测,异常阈值由自编码器的重构误差来确定;最
后将提出的模型在公共数据集上进行了验证。 结果 用真实事件日志对提出的方法进行评估,与其他方法进行对比
分析可知,所提出的方法在精确度、召回率和 F1 分数 3 个方面都有较好的表现,且所提出的模型 AUC 在所有数据
集上都达到了较大的值。 结论 实验结果表明:所提出的方法可以更好地检测过程事件日志中的异常;通过在模型
中加入注意力机制并且将控制流和数据流视角进行结合,更好地表示了过程数据,使得模型的分类性能得到了较
大的提升,在业务流程异常检测方面具有明显的优势。 |
| 关键词: 自注意力机制 Bi-LSTM 神经网络 业务流程 异常检测 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Anomaly Detection Model for Business Process Based on Self-attention and Bi-LSTM |
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CHEN Guowei1 LU Ke1 2
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1. School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan 232001 China
2. Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety
Anhui Huainan 232001 China
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| Abstract: |
| An important task in business processes is anomaly detection of business process data which can be used to
monitor and identify abnormal situations in enterprises or organizations. Objective Most current methods for business
process anomaly detection only consider the control flow and do not consider the influence of other data attributes in event
logs on business processes. Therefore an unsupervised anomaly detection model with multiple perspectives was proposed.
Methods Firstly the control flow and data flow were processed separately and then spliced to form the input data type for
the model. Secondly a model combining the self-attention mechanism and Bi-LSTM autoencoder was used to extract
features from the perspectives of control flow and data flow of business process event logs respectively and then splicing
was carried out for anomaly detection with the anomaly threshold determined by the reconstruction error of the
autoencoder. Finally the proposed model was validated on public datasets. Results The proposed method was evaluated
using real event logs and a comparative analysis with other methods showed that the proposed method performed better in
three aspects precision recall and F1 score and the AUC of the proposed model reached large values on all datasets. Conclusion Experimental results show that the proposed method can better detect anomalies in process event logs. By
incorporating attention mechanisms into the model and combining control flow and data flow perspectives a better
representation of process data is achieved leading to significantly improved classification performance and clear
advantages in business process anomaly detection. |
| Key words: self-attention mechanism Bi-LSTM neural network business process anomaly detection |
