Detecção de Falhas em um Aplicativo Móvel Bancário

  • Daniel Schulz UnB
  • Marcelo Marotta UnB
  • Lucas Bondan RNP / UnB
  • Marcos Caetano UnB
  • Geraldo P. Rocha Filho UESB / UnB
  • Aleteia Araujo UnB


The internet has changed the way banks deliver services to customers. Due to the high number of digital accesses, interruptions in Information Systems cause great damage to the financial system. One of the strategies implemented to achieve a stable environment is the continuous monitoring of services, as described by ITIL. Given the above, this work proposes a failure detection approach through data mining techniques using the CRISP-DM reference model. The approach involves evaluating data extracted from a web analytics tool, in real time, to identify critical failures in a mobile banking application. The effects of different feature engineering techniques, such as variable filtering, data standardization and synthetic sample generation, were evaluated in 7 classification algorithms. Finally, the results were compared, and the support vector machine was the one that obtained the best result, with an F1-Score of 0.954 and a ROC-AUC of 0.989.


Afshan, S. and Sharif, A. (2016). Acceptance of mobile banking framework in pakistan. Telematics and Informatics, 33(2):370-387.

Beyer, B., Jones, C., Petoff, J., and Murphy, N. R. (2016). Site reliability engineering: How Google runs production systems. "O'Reilly Media, Inc.".

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R., et al. (2000). Crisp-dm 1.0: Step-by-step data mining guide. SPSS inc, 9(13):1-73.

Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321-357.

Chen, X., Lu, C.-D., and Pattabiraman, K. (2014). Failure prediction of jobs in compute clouds: A google cluster case study. In 2014 IEEE International Symposium on Software Reliability Engineering Workshops, pages 341-346. IEEE.

Dai Vu, D., Vu, X. T., and Kim, Y. (2021). Deep learning-based fault prediction in cloud system. In 2021 International Conference on Information and Communication Technology Convergence (ICTC), pages 1826-1829. IEEE.

Du, M., Li, F., Zheng, G., and Srikumar, V. (2017). Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pages 1285-1298.

Duarte, A., Frost, J., Gambacorta, L., Koo Wilkens, P., and Shin, H. S. (2022). Central banks, the monetary system and public payment infrastructures: lessons from brazil's pix. Available at SSRN 4064528.

Galup, S. D., Dattero, R., Quan, J. J., and Conger, S. (2009). An overview of it service management. Communications of the ACM, 52(5):124-127.

Gao, J., Wang, H., and Shen, H. (2020). Task failure prediction in cloud data centers using deep learning. IEEE transactions on services computing.

Jain, R. (2008). The art of computer systems performance analysis. john wiley & sons.

Limited, A. (2019). ITIL foundation: ITIL 4 edition. TSO (The Stationery Office), ein Unternehmen von Williams Lea.

Malaquias, R. F. and Hwang, Y. (2019). Mobile banking use: A comparative study with brazilian and us participants. International Journal of Information Management, 44:132-140.

Meirelles, F. d. S. (2022). Pesquisa anual do uso de ti. Fundação Getúlio Vargas.

Mohamad, I. B. and Usman, D. (2013). Standardization and its effects on k-means clustering algorithm. Research Journal of Applied Sciences, Engineering and Technology, 6(17):3299-3303.

Munir, M., Siddiqui, S. A., Dengel, A., and Ahmed, S. (2018). Deepant: A deep learning approach for unsupervised anomaly detection in time series. Ieee Access, 7:1991-2005.

O'Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., Invernizzi, L., et al. (2019). Kerastuner.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.

Phippen, A., Sheppard, L., and Furnell, S. (2004). A practical evaluation of web analytics. Internet Research.

Raju, V. G., Lakshmi, K. P., Jain, V. M., Kalidindi, A., and Padma, V. (2020). Study the influence of normalization/transformation process on the accuracy of supervised classification. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pages 729-735. IEEE.

Singh, D. and Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97:105524.

Yuan, Y., Shi, W., Liang, B., and Qin, B. (2019). An approach to cloud execution failure diagnosis based on exception logs in openstack. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pages 124-131. IEEE.

Zhang, K., Xu, J., Min, M. R., Jiang, G., Pelechrinis, K., and Zhang, H. (2016). Automated it system failure prediction: A deep learning approach. In 2016 IEEE International Conference on Big Data (Big Data), pages 1291-1300. IEEE.

Zhang, X., Xu, Y., Lin, Q., Qiao, B., Zhang, H., Dang, Y., Xie, C., Yang, X., Cheng, Q., Li, Z., et al. (2019). Robust log-based anomaly detection on unstable log data. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 807-817.
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SCHULZ, Daniel; MAROTTA, Marcelo; BONDAN, Lucas; CAETANO, Marcos; ROCHA FILHO, Geraldo P.; ARAUJO, Aleteia. Detecção de Falhas em um Aplicativo Móvel Bancário. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 41. , 2023, Brasília/DF. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 365-378. ISSN 2177-9384. DOI:

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