Implementing and automating business processes often means to connect and integrate a diverse set of potentially flawed services and applications. This makes them an attractive target for attackers. Here anomaly detection is one of the last defense lines against unknown vulnerabilities. Whereas anomaly detection for process behavior has been researched, anomalies in process instance data have been neglected so far, even though the data is exchanged with external services and hence might be a major sources for attacks. Deriving the required anomaly detection signatures can be a complex, work intensive, and error-prone task, specifically at the presence of a multitude of process versions and instances. Hence, this paper proposes a novel automatic signature generation approach for textual business process instance data while respecting its contextual attributes. Its efficiency is shown by an comprehensive evaluation that applies the approach on thousands of realistic data entries and 240,000 anomalous data entries.

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