Abstract
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.
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References
Aalst, W.: Data science in action. In: Process Mining, pp. 3–23. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4_1
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate (2014)
Böhmer, K., Rinderle-Ma, S.: Multi-perspective anomaly detection in business process execution events. In: Debruyne, C., et al. (eds.) OTM 2016. LNCS, vol. 10033, pp. 80–98. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48472-3_5
Böhmer, K., Rinderle-Ma, S.: Multi instance anomaly detection in business process executions. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 77–93. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_5
Böhmer, K., Rinderle-Ma, S.: Association rules for anomaly detection and root cause analysis in process executions. In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 3–18. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_1
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling (2014)
Eskin, E.: Anomaly detection over noisy data using learned probability distributions (2000)
Holtzman, A., Buys, J., Forbes, M., Choi, Y.: The curious case of neural text degeneration. CoRR abs/1904.09751 (2019). http://arxiv.org/abs/1904.09751
Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. IJDKP 5(2), 1 (2015)
Huo, S., Völzer, H., Reddy, P., Agarwal, P., Isahagian, V., Muthusamy, V.: Graph autoencoders for business process anomaly detection. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 417–433. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85469-0_26
Leitner, M., Rinderle-Ma, S.: A systematic review on security in process-aware information systems - constitution, challenges, and future directions. Inf. Softw. Technol. 56(3), 273–293 (2014). https://doi.org/10.1016/j.infsof.2013.12.004
Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_31
Mavroudopoulos, I., Gounaris, A.: Detecting temporal anomalies in business processes using distance-based methods. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds.) DS 2020. LNCS (LNAI), vol. 12323, pp. 615–629. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61527-7_40
Meng, W., et al.: Device-agnostic log anomaly classification with partial labels. In: IWQoS 2018, pp. 1–6 (2018)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)
Nedelkoski, S., Cardoso, J.S., Kao, O.: Anomaly detection and classification using distributed tracing and deep learning. In: CCGRID 2019, pp. 241–250. IEEE (2019)
Nolle, T., Luettgen, S., Seeliger, A., Mühlhäuser, M.: Analyzing business process anomalies using autoencoders. Mach. Learn. 107(11), 1875–1893 (2018)
Nolle, T., Seeliger, A., Mühlhäuser, M.: BINet: multivariate business process anomaly detection using deep learning. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 271–287. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_16
Pauwels, S., Calders, T.: Incremental predictive process monitoring: the next activity case. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 123–140. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85469-0_10
Rogge-Solti, A., Kasneci, G.: Temporal anomaly detection in business processes. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 234–249. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10172-9_15
Rud, D., Schmietendorf, A., Dumke, R.R.: Product metrics for service-oriented infrastructures. In: IWSM/MetriKon 2006 (2006)
Rudolf, N.: Profile-based Anomaly Detection in Service Oriented Business Processes. master thesis, University of Vienna (2023)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. CoRR abs/1409.3215 (2014). http://arxiv.org/abs/1409.3215
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Rudolf, N., Böhmer, K., Leitner, M. (2024). BAnDIT: Business Process Anomaly Detection in Transactions. In: Sellami, M., Vidal, ME., van Dongen, B., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2023. Lecture Notes in Computer Science, vol 14353. Springer, Cham. https://doi.org/10.1007/978-3-031-46846-9_22
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