Skip to main content
SpringerLink
Log in

A Practice-Oriented, Control-Flow-Based Anomaly Detection Approach for Internal Process Audits

  • Conference paper
  • First Online:
Service-Oriented Computing (ICSOC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12571))

Included in the following conference series:

Abstract

Internal auditing tries to identify anomalies, weaknesses and manipulations in business processes in order to protect the company from risks. Due to the digitalization of processes, auditors also have to check the associated data volumes. Already existing IT-systems focus on process-related data where the control flow, i.e. the actual sequence of process events, is not considered. This paper examines how the control flow and the process-related data can be analyzed in combination to support auditors in process auditing. To realize this, audit requirements were collected in the literature and evaluated by auditors from industry. On this basis, a concept with five indicators was developed, then transferred into a prototype and evaluated using real-life data as well as two auditors. The results show that the requirements can be technically realized and the developed indicators enable auditors to identify and interpret abnormal process executions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Van der Aalst, W.M.P.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4

    Book  Google Scholar 

  2. 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_28

    Chapter  Google Scholar 

  3. Jans, M., Alles, M., Vasarhelyi, M.: A field study on the use of process mining of event logs as an analytical procedure in auditing. Account. Rev. 89(5), 1751–1773 (2014)

    Article  Google Scholar 

  4. Hosseinpour, M., Jans, M.: Process deviation categories in an auditing context. Available at SSRN 3280339 (2019)

    Google Scholar 

  5. Nolle, T., Luettgen, S., Seeliger, A., Mühlhäuser, M.: Analyzing business process anomalies using autoencoders. Mach. Learn. 107(11), 1875–1893 (2018). https://doi.org/10.1007/s10994-018-5702-8

    Article  MathSciNet  MATH  Google Scholar 

  6. Hosseinpour, M., Jans, M.: Categorizing identified deviations for auditing. In: SIMPDA 2016, Graz, Austria. pp. 125–129 (2016)

    Google Scholar 

  7. de Leoni, M., van der Aalst, W.M.P., van Dongen, B.F.: Data- and resource-aware conformance checking of business processes. In: Abramowicz, W., Kriksciuniene, D., Sakalauskas, V. (eds.) BIS 2012. LNBIP, vol. 117, pp. 48–59. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30359-3_5

    Chapter  Google Scholar 

  8. de Leoni, M., van der Aalst, W.M.P.: Aligning event logs and process models for multi-perspective conformance checking: an approach based on integer linear programming. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 113–129. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40176-3_10

    Chapter  Google Scholar 

  9. Borrego, D., Barba, I.: Conformance checking and diagnosis for declarative business process models in data-aware scenarios. Expert Syst. Appl. 41(11), 5340–5352 (2014)

    Article  Google Scholar 

  10. Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models. Expert Syst. Appl. 65, 194–211 (2016)

    Article  Google Scholar 

  11. Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407–437 (2015). https://doi.org/10.1007/s00607-015-0441-1

    Article  MathSciNet  MATH  Google Scholar 

  12. Böhmer, K., Rinderle-Ma, S.: Multi-perspective anomaly detection in business process execution events. In: Debruyne, C., Panetto, H., Meersman, R., Dillon, T., Kühn, e, O’Sullivan, D., Ardagna, C.A. (eds.) OTM 2016. LNCS, vol. 10033, pp. 80–98. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48472-3_5

    Chapter  Google Scholar 

  13. Böhmer, K., Rinderle-Ma, S.: Association rules for anomaly detection and root cause analysis in process executions. In: Krogstie, J., Reijers, Hajo A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 3–18. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_1

    Chapter  Google Scholar 

  14. Alizadeh, M., Lu, X., Fahland, D., Zannone, N., van der Aalst, W.M.P.: Linking data and process perspectives for conformance analysis. Comput. Secur. 73, 172–193 (2018)

    Article  Google Scholar 

  15. Van der Aalst, W.M.P., van Hee, K. M., van Werf, J.M., Verdonk, M.: Auditing 2.0: using process mining to support tomorrow’s auditor. Computer 43(3), 90–93 (2010)

    Google Scholar 

  16. Jans, M., Alles, M., Vasarhelyi, M.: Process mining of event logs in internal auditing: a case study. In: 2nd International Symposium on Accounting Information Systems, Rome (2012)

    Google Scholar 

  17. Barboza, T.M., Santoro, F.M., Revoredo, K.C., Costa, R.M.M.: A case study of process mining in auditing. In: Proceedings of the XV Brazilian Symposium on Information Systems, pp. 1–8 (2019)

    Google Scholar 

  18. De Leoni, M., van der Aalst, W.M.P.: Data-aware process mining: discovering decisions in processes using alignments. In: Proceedings of the 28th Annual ACM Symposium on applied Computing, pp. 1454–1461. ACM Press (2013)

    Google Scholar 

  19. Das, S., Cakmak, U.M.: Hands-On Automated Machine Learning: A Beginner’s Guide to Building Automated Machine Learning Systems Using AutoML and Python. Packt Publishing, Birmingham (2018)

    Google Scholar 

  20. Chollet, F.: Deep Learning with Python. Manning Publications, Shelter Island (2017)

    Google Scholar 

  21. ProM. http://www.processmining.org/prom/start. Accessed 19 Apr 2020

  22. Leemans, S.: Inductive visual Miner & Directly Follows visual Miner – manual (2019)

    Google Scholar 

  23. Tax, N., Sidorova, N., van der Aalst, W.M.P.: Discovering more precise process models from event logs by filtering out chaotic activities. J. Intell. Inf. Syst. 52(1), 107–139 (2019)

    Article  Google Scholar 

  24. Conforti, R., La Rosa, M., ter Hofstede, A.H.M.: Filtering out infrequent behavior from business process event logs. IEEE Trans. Knowl. Data Eng. 29(2), 300–314 (2017)

    Article  Google Scholar 

  25. Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation forest. In: Proceedings of the Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE Computer Society (2008)

    Google Scholar 

  26. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. ACM Sigmod Rec. 29(2), 93–104 (2000)

    Article  Google Scholar 

  27. Sklearn. https://scikit-learn.org/stable/modules/outlier_detection.html. Accessed 24 Apr 2020

  28. Nguyen, T.T., Nguyen, A.T., Nguyen, T.A.H., Vu, L.T., Nguyen, Q.U., Hai, L.D.: Unsupervised anomaly detection in online game. In: Proceedings of the Sixth International Symposium on Information and Communication Technology, pp. 4–10, ACM (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Oldenburg, 26129, Oldenburg, NI, Germany

    Gerrit Schumann, Felix Kruse & Jakob Nonnenmacher

Authors
  1. Gerrit Schumann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Felix Kruse
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jakob Nonnenmacher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerrit Schumann .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Eleanna Kafeza

  2. University of New South Wales, Sydney, NSW, Australia

    Boualem Benatallah

  3. IIT National Research Council C.N.R., Pisa, Italy

    Fabio Martinelli

  4. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  5. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  6. Ernst & Young AI Lab, San Jose, CA, USA

    Hamid Motahari

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Schumann, G., Kruse, F., Nonnenmacher, J. (2020). A Practice-Oriented, Control-Flow-Based Anomaly Detection Approach for Internal Process Audits. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-65310-1_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65309-5

  • Online ISBN: 978-3-030-65310-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation