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.
The approach within this paper is highly influenced by the works in @cite_5 @cite_26 @cite_31 , in which they propose the use of replicator neural networks @cite_15 for anomaly detection, i.e., networks that reproduce their input, which are based on the idea of autoencoders from @cite_27 . Autoassociative neural network encoders use a similar concept and have been used to model the nominal behavior of complex systems @cite_28 . They have also been used for residual generation in @cite_29 , demonstrating that these models can also model behavior not directly observed in the training data, which increases generalization. A comprehensive study of replicator neural networks for outlier detection can be found in @cite_32 . The approaches from @cite_29 @cite_5 @cite_26 @cite_31 @cite_28 , however, do not work well with variable length input. In our approach, we address this problem by using a padding technique. We opted to use a neural network based approach, for recent achievements in machine translation and natural language processing indicate that neural networks are an excellent choice when modeling sequential data @cite_44 @cite_10 .
@cite_26 @cite_15 @cite_28 @cite_29 @cite_32 @cite_44 @cite_27 @cite_5 @cite_31 @cite_10
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