Anomalies Detection in the Application Logs Using Kohonen SOM Machine Learning Algorithm
Anomalies Detection in the Application Logs Using Kohonen SOM Machine Learning Algorithm
Autori:
Izdanje: International Scientific Conference on Information Technology and Data Related Research
DOI: 10.15308/Sinteza-2020-275-282
Oblast: Advanced Computing
Stranice: 275-282
Apstrakt:
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.
Ključne reči: internal fraud, Self-Organizing Mapping, Kohonen, path analysis, detection anomalies
Priložene datoteke:
- 275-282 ( veličina: 624,27 KB, broj pregleda: 377 )
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@article{article, author = {V. Maksimović, M. Marjanović and A. Njeguš}, title = {Anomalies Detection in the Application Logs Using Kohonen SOM Machine Learning Algorithm}, journal = {International Scientific Conference on Information Technology and Data Related Research}, year = 2020, pages = {275-282}, doi = {10.15308/Sinteza-2020-275-282} }
RT Conference Proceedings A1 Vladimir Maksimović A1 Marina Marjanović A1 Angelina Njeguš T1 Anomalies Detection in the Application Logs Using Kohonen SOM Machine Learning Algorithm AD Univerzitet Singidunum, Beograd, Beograd, Srbija YR 2020 NO doi: 10.15308/Sinteza-2020-275-282
V. Maksimović, M. Marjanović and A. Njeguš, Anomalies Detection in the Application Logs Using Kohonen SOM Machine Learning Algorithm, Univerzitet Singidunum, Beograd, 2020, doi:10.15308/Sinteza-2020-275-282