Modeling of anomaly detection processes in complex structured monitoring data

by

A.A. Bryukhovetskiy1, A.V. Skatkov1, Y.E. Shishkin1,2

1Federal State Educational Institution of Higher Education «Sevastopol State University», Russian Federation, Sevastopol, Universitetskaya St., 33

2Institute of Natural and Technical Systems, Russian Federation, Sevastopol, Lenin St., 28

E-mail: yourockpro@gmail.com

DOI: 10.33075/2220-5861-2017-3-45-49

UDC 004.942:504.064.36

Abstract:

   The paper introduces an approach to solve the problem of detecting a change in the state of the monitoring data stream using normal distribution models based on Spearman’s nonparametric statistics criterion. The problem of detecting anomalies in complex-structured monitoring data for critical-purpose systems is discussed. The decision of agent generation intensity values influence estimation problem, intensity of applications service, system loading, samples volume, time of characteristics measurement, intervals of characteristics measurement time and significance levels on change of a monitoring object condition is resulted. The results of probabilistic simulation of a critical object are discussed.

Keywords: complex structured data, monitoring, simulation, queuing system, Big Data, heteroscedasticity effect, network traffic, complex systems modeling.

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