Using the methods and algorithms for data analysis and machine learning in UEBA/DSS to support management decision-making

The aim of this study is to develop mathematical and software for detecting abnormal user behavior based on an analysis of their behavioral biometric characteristics to create new ways to provide analytical data to the analyzing service with a description of why the identified actions are considered abnormal. The subject of the study is the machine learning methods used in UBA / UEBA (User Behavioral Analytics / User and Entity Behavioral Analytics), DLP (Data Leak Prevention), SIEM (Security information and event management) systems. Object of study - UBA / UEBA, DLP, SIEM systems. This article provides an overview of the applicability of machine learning methods in intelligent UEBA / DSS systems. One of the significant problems in intelligent UEBA / DSS systems is obtaining useful information from a large amount of unstructured, inconsistent data. The methods and algorithms of intelligent data processing and machine learning used in UEBA / DSS systems make it possible to solve data analysis problems of various kinds. The application of machine learning methods in the implementation of a mobile UEBA / DSS system is proposed. This will allow to achieve high quality data analysis and find complex dependencies in them. During the study, a list of the most significant factors submitted to the input of the analyzing methods was formed. The application of machine learning methods in UEBA / DSS systems will allow you to make informed management decisions and reduce the time to obtain useful information.

1. Cai L., Zhu Y. The challenges of data quality and data quality assessment in the big data era. Data science journal.2015;14.

2. Cao J. et al. Big data: A parallel particle swarm optimization-back-propagation neural network algorithm based on MapReduce .PloS one. 2016;11(6).

3. Chen H., Chiang R. H. L., Storey V. C. Business intelligence and analytics: From big data to big impact .MIS quarterly. 2012;36(4).

4. Dutt A., Ismail M. A., Herawan T. A systematic review on educational data mining .IEEE Access. – 2017;5.

5. Ivutin A. N., Savenkov P. A., Veselova A. V. Neural network for analysis of additional authentication behavioral biometrie characteristics .2018 7th Mediterranean Conference on Embedded Computing (MECO). 2018;(7).

6. Wang J., Neskovic P., Cooper L. N. Improving nearest neighbor rule with a simple adaptive distance measure .Pattern Recognition Letters.2007;28(2):207-213.

7. Yan Z. et al. Energy-efficient continuous activity recognition on mobile phones: An activityadaptive approach .2012 16th international symposium on wearable computers. – Ieee, 2012;16.