Businesses are naturally interested in detecting anomalies in their internal processes, because these can be indicators for fraud and inefficiencies. Within the domain of business intelligence, classic anomaly detection is not very frequently researched. In this paper, we propose a method, using autoencoders, for detecting and analyzing anomalies occurring in the execution of a business process. Our method does not rely on any prior knowledge about the process and can be trained on a noisy dataset already containing the anomalies. We demonstrate its effectiveness by evaluating it on 700 different datasets and testing its performance against three state-of-the-art anomaly detection methods. This paper is an extension of our previous work from 2016 ( in Unsupervised anomaly detection in noisy business process event logs using denoising autoencoders. In: International conference on discovery science, Springer, pp 442---456, 2016). Compared to the original publication we have further refined the approach in terms of performance and conducted an elaborate evaluation on more sophisticated datasets including real-life event logs from the Business Process Intelligence Challenges of 2012 and 2017. In our experiments our approach reached an @math F1 score of 0.87, whereas the best unaltered state-of-the-art approach reached an @math F1 score of 0.72. Furthermore, our approach can be used to analyze the detected anomalies in terms of which event within one execution of the process causes the anomaly.
Ensuring anomaly-free process model executions is crucial in order to prevent fraud and security breaches. Existing anomaly detection approaches focus on the control flow, point anomalies, and struggle with false positives in the case of unexpected events. By contrast, this paper proposes an anomaly detection approach that incorporates perspectives that go beyond the control flow, such as, time and resources (i.e., to detect contextual anomalies). In addition, it is capable of dealing with unexpected process model execution events: not every unexpected event is immediately detected as anomalous, but based on a certain likelihood of occurrence, hence reducing the number of false positives. Finally, multiple events are analyzed in a combined manner in order to detect collective anomalies. The performance and applicability of the overall approach are evaluated by means of a prototypical implementation along and based on real life process execution logs from multiple domains.