Multi Instance Anomaly Detection in Business Process Executions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10445)

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.

Keywords

Runtime anomaly detection Secure business processes Multiple instances Temporal anomalies 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer ScienceUniversity of ViennaViennaAustria

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