Business process anomaly detection enables the prevention
of misuse and failures. Existing approaches focus on detecting anomalies
in control, temporal, and resource behavior of individual instances, neglecting
the communication of multiple instances in choreographies. Consequently,
anomaly detection capabilities are limited. This study presents
a novel neural network-based approach to detect anomalies in distributed
business processes. Unlike existing methods, our solution considers message
data exchanged during process transactions. Allowing the generation
of detection profiles incorporating the relationship between multiple
instances, related services, and exchanged data to detect point and contextual
anomalies during process runtime. To validate the proposed solution,
it is demonstrated with a prototype implementation and validated
with a use case from the ecommerce domain. Future work aims to further
improve the deep learning approach, to enhance detection performance.