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Multi Instance Anomaly Detection in Business Process Executions

Abstract

Processes control critical IT systems and business cases in dynamic environments. Hence, ensuring secure model executions is crucial to prevent misuse and attacks. In general, anomaly detection approaches can be employed to tackle this challenge. Existing ones analyze each process instance individually. Doing so does not consider attacks that combine multiple instances, e.g., by splitting fraudulent fund transactions into multiple instances with smaller unsuspicious amounts. The proposed approach aims at detecting such attacks. For this, anomalies between the temporal behavior of a set of historic instances (ex post) and the temporal behavior of running instances are identified. Here, temporal behavior refers to the temporal order between the instances and their events. The proposed approach is implemented and evaluated based on real life process logs from different domains and artificial anomalies.

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Citation
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
Int'l Conference on Business Process Management 2017
Divisions
Workflow Systems and Technology
Event Location
Barcelona, Spain
Event Type
Conference
Event Dates
10-15 September 2017
Page Range
pp. 77-93
Date
2017
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