Anomalies Detection in the Application Logs Using Kohonen SOM Machine Learning Algorithm


DOI: https://doi.org/10.15308/Sinteza-2020-275-282
Authors:
Vladimir S. Marković
Marina Marjanović Jakovljević
Angelina Njeguš

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Keywords:
internal fraud, Self-Organizing Mapping, Kohonen, path analysis, detection anomalies


Abstract:
Internal fraud in the financial sector are difficult to detect since fraudulent transactions are indistinguishable from ordinary transactions, and standard checkpoints, in the form of transaction documentation and authorization, are skillfully avoided. Well-designed software has available and machine-readable application logs that can be analyzed to detect anomalies in application usage. This paper presents a data preparation technique using path analysis and Kohonen SOM clustering algorithm that can help better profile users of an application to reduce the number of cases that will be further investigated.

CITATION:

IEEE format

V. S. Marković, M. Marjanović Jakovljević, A. Njeguš, “Anomalies Detection in the Application Logs Using Kohonen SOM Machine Learning Algorithm,” in Sinteza 2020 - International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2020, pp. 275-282. doi:10.15308/Sinteza-2020-275-282

APA format

S. Marković, V., Marjanović Jakovljević, M., Njeguš, A. (2020). Anomalies Detection in the Application Logs Using Kohonen SOM Machine Learning Algorithm. Paper presented at Sinteza 2020 - International Scientific Conference on Information Technology and Data Related Research. doi:10.15308/Sinteza-2020-275-282

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