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Business Process Management Systems (BPMS) log events and traces ofactivities during the execution of a process. Anomalies are defined asdeviation or departure from the normal or common order. Anomaly detection inbusiness process logs has several applications such as fraud detection andunderstanding the causes of process errors. In this paper, we present a novelapproach for anomaly detection in business process logs. We model the eventlogs as a sequential data and apply kernel based anomaly detection techniquesto identify outliers and discordant observations. Our technique is unsupervised(does not require a pre-annotated training dataset), employs kNN (k-nearestneighbor) kernel based technique and normalized longest common subsequence(LCS) similarity measure. We conduct experiments on a recent, large andreal-world incident management data of an enterprise and demonstrate that ourapproach is effective.

Kernel Based Sequential Data Anomaly Detection in Business Process Event Logs