
Digital
Library of the European Council for Modelling and Simulation 
Title: 
Statistical
Classification In Monitoring Systems 
Authors: 
Alexander A. Grusho,
Nick A. Grusho, Elena E. Timonina 
Published in: 
(2016).ECMS 2016 Proceedings edited
by: Thorsen Claus, Frank Herrmann, Michael Manitz, Oliver Rose, European Council for Modeling and
Simulation. doi:10.7148/2016 ISBN:
9780993244025 30^{th}
European Conference on Modelling and Simulation, Regensburg Germany, May 31^{st}
– June 3^{rd}, 2016 
Citation
format: 
Alexander A. Grusho,
Nick A. Grusho, Elena E. Timoninaj
(2016). Statistical Classification In Monitoring Systems, ECMS 2016
Proceedings edited by: Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose European Council for Modeling
and Simulation. doi:10.7148/20160658 
DOI: 
http://dx.doi.org/10.7148/20160658 
Abstract: 
The paper is devoted to the
statistical classification problems. Repeated classification in control and
monitoring systems is complicated by nonzero mistakes of traditional
statistical decisions. At repeated applications of rules of statistical
classification small probabilities of mistakes generate a large number of
wrong decisions. At construction of monitoring systems of information security
in computer systems wrong decisions are especially dangerous. Therefore for
construction of secure architecture of control and monitoring systems it is
necessary to look for nonconventional statistical decisions. In finite set of words of finite
length the ban is the word having zero probability of appearance. If the
statistical criterion has the critical set consisting of only bans of
supposed probability measure, the probability of wrong rejection of this
measure is equal to zero. Therefore repeated application of such criterion
won’t generate to false alarms in monitoring systems. In the paper we consider a case of
statistical classification when classes are defined by finite sets of
probability distributions on a space of infinite sequences. We use bans to
define decision functions and prove conditions when these decisions produce
no mistakes. 
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