Nolle, Timo ; Seeliger, Alexander ; Mühlhäuser, Max
Weske, Mathias ; Montali, Marco ; Weber, Ingo ; vom Brocke, Jan (eds.) (2018):
BINet: Multivariate Business Process Anomaly Detection Using Deep Learning.
11080, pp. 271-287, Springer, Sydney, Australia, September 9-14, 2018, ISBN 978-3-319-98647-0,
DOI: 10.1007/978-3-319-98648-7_16,
[Conference or Workshop Item]

Abstract

In this paper, we propose BINet, a neural network architecture for real-time multivariate anomaly detection in business process event logs. BINet has been designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a heuristic for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level, but also on event attribute level. We compare BINet to 6 other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 60 synthetic and 21 real life event logs using artificial anomalies. BINet reached an average F₁ score over all detection levels of 0.83, whereas the next best approach, a denoising autoencoder, reached only 0.74. This F₁ score is calculated over two different levels of detection, namely case and attribute level. BINet reached 0.84 on case and 0.82 on attribute level, whereas the next best approach reached 0.78 and 0.71 respectively.

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