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BINet: Multivariate Business Process Anomaly Detection Using Deep Learning

  • Timo NolleEmail author
  • Alexander Seeliger
  • Max Mühlhäuser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11080)

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 \(F_1\) score over all detection levels of 0.83, whereas the next best approach, a denoising autoencoder, reached only 0.74. This \(F_1\) 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.

Keywords

Business process management Anomaly detection Artificial process intelligence Deep learning Recurrent neural networks 

Notes

Acknowledgements

This project [522/17-04] is funded in the framework of Hessen ModellProjekte, financed with funds of LOEWE, Förderlinie 3: KMU-Verbundvorhaben (State Offensive for the Development of Scientific and Economic Excellence), and by the German Federal Ministry of Education and Research (BMBF) Software Campus project “AI-PM” [01IS17050].

References

  1. 1.
    van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49851-4CrossRefGoogle Scholar
  2. 2.
    Bezerra, F., Wainer, J.: Anomaly detection algorithms in logs of process aware systems. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 951–952. ACM (2008)Google Scholar
  3. 3.
    Bezerra, F., Wainer, J.: Algorithms for anomaly detection of traces in logs of process aware information systems. Inf. Syst. 38(1), 33–44 (2013)CrossRefGoogle Scholar
  4. 4.
    Bezerra, F., Wainer, J., van der Aalst, W.M.P.: Anomaly detection using process mining. In: Halpin, T., et al. (eds.) BPMDS/EMMSAD -2009. LNBIP, vol. 29, pp. 149–161. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01862-6_13CrossRefGoogle Scholar
  5. 5.
    Böhmer, K., Rinderle-Ma, S.: Multi-perspective anomaly detection in business process execution events. In: Debruyne, C., et al. (eds.) Move to Meaningful Internet Systems. LNCS, pp. 80–98. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-48472-3_5CrossRefGoogle Scholar
  6. 6.
    Burattin, A.: PLG2: multiperspective processes randomization and simulation for online and offline settings. arXiv:1506.08415 (2015)
  7. 7.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection for discrete sequences: a survey. IEEE Trans. Knowl. Data Eng. 24(5), 823–839 (2012)CrossRefGoogle Scholar
  8. 8.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078 (2014)
  9. 9.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  10. 10.
    Evermann, J., Rehse, J.-R., Fettke, P.: A deep learning approach for predicting process behaviour at runtime. In: Dumas, M., Fantinato, M. (eds.) BPM 2016. LNBIP, vol. 281, pp. 327–338. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58457-7_24CrossRefGoogle Scholar
  11. 11.
    Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129–140 (2017)CrossRefGoogle Scholar
  12. 12.
    Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, New York City (2011)zbMATHGoogle Scholar
  13. 13.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  14. 14.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)Google Scholar
  15. 15.
    Japkowicz, N.: Supervised versus unsupervised binary-learning by feedforward neural networks. Mach. Learn. 42(1), 97–122 (2001)CrossRefGoogle Scholar
  16. 16.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)
  17. 17.
    Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., Shroff, G.: LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv:1607.00148 (2016)
  18. 18.
    Marchi, E., Vesperini, F., Eyben, F., Squartini, S., Schuller, B.: A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks, April 2015Google Scholar
  19. 19.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013)
  20. 20.
    Nolle, T., Luettgen, S., Seeliger, A., Mühlhäuser, M.: Analyzing business process anomalies using autoencoders. arXiv:1803.01092 (2018)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Nolle, T., Seeliger, A., Mühlhäuser, M.: Unsupervised anomaly detection in noisy business process event logs using denoising autoencoders. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 442–456. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46307-0_28CrossRefGoogle Scholar
  22. 22.
    Pimentel, M.A.F., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Sig. Process. 99, 215–249 (2014)CrossRefGoogle Scholar
  23. 23.
    Schölkopf, B., et al.: Support vector method for novelty detection. In: NIPS. vol. 12, pp. 582–588 (1999)Google Scholar
  24. 24.
    Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 477–492. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59536-8_30CrossRefGoogle Scholar
  25. 25.
    Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc.: Ser. B (Stat. Methodol.) 63(2), 411–423 (2001)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Warrender, C., Forrest, S., Pearlmutter, B.: Detecting intrusions using system calls: alternative data models. In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 133–145. IEEE (1999)Google Scholar
  27. 27.
    Wen, L., van der Aalst, W.M.P., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Min. Knowl. Disc. 15(2), 145–180 (2007)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Wressnegger, C., Schwenk, G., Arp, D., Rieck, K.: A close look on n-grams in intrusion detection: Anomaly detection vs. classification. In: Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Security, pp. 67–76. AISec 2013. ACM (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Timo Nolle
    • 1
    Email author
  • Alexander Seeliger
    • 1
  • Max Mühlhäuser
    • 1
  1. 1.Telecooperation LabTechnische Universität DarmstadtDarmstadtGermany

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