Business process anomaly detection based on concept drifting discovery
The existing business process anomaly detection methods assume that the business process is fixed and ig-nore the change of the business process model due to the conceptual drift,resulting in the decrease of the accuracy of the existing anomaly detection methods.For this reason,a business process anomaly detection method based on con-cept drift discovery using event log was proposed.A business process anomaly detection model was built based on Recurrent Neural Network(RNN)combined with concept drift discovery method.The data set with event sequence features was extracted from the event log.The event prediction module in the model was used to predict the proba-bility of event occurrence,and the anomaly score of each case in the event log was calculated according to the proba-bility distribution of event occurrence.The case of which the anomaly score was greater than the anomaly score threshold was considered as a candidate abnormal case.Hoeffding's inequality was used to judge whether the concept drifting had occurred,and the double-layer sliding window mechanism was used to obtain the locations of the con-cept drifting cases and extract the concept drifting cases.Using incremental learning,the event prediction module was update with concept drifting cases,so that the business process anomaly detection model could distinguish the concept drifting cases from the true anomaly cases,and more accurately detect the true business process anoma-lies.The experimental results showed that,compared with the mainstream business process anomaly detection methods,the proposed anomaly detection method could more accurately detect the conceptual drifting in the business process,and could more accurately detect the anomalies in the business process.The proposed method was of important significance to improve the stability of the business process.
business processanomaly detectionconcept driftingsliding windowanomaly score threshold
NETL
NSTL
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