Informatika i Ee Primeneniya [Informatics and its Applications]
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive
Impact factor

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS



Inform. Primen.:
Year:
Volume:
Issue:
Page:
Find






Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register


Informatika i Ee Primeneniya [Informatics and its Applications], 2020, Volume 14, Issue 3, Pages 71–75
DOI: https://doi.org/10.14357/19922264200310
(Mi ia681)
 

This article is cited in 1 scientific paper (total in 1 paper)

Mathematical statistics in the task of identifying hostile insiders

A. A. Grushoa, M. I. Zabezhailob, D. V. Smirnovc, E. E. Timoninaa, S. Ya. Shorgina

a Institute of Informatics Problems, Federal Research Center “Computer Sciences and Control” of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119133, Russian Federation
b A. A. Dorodnicyn Computing Center, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 40 Vavilov Str., Moscow 119333, Russian Federation
c Sberbank of Russia, 19 Vavilov Str., Moscow 117999, Russian Federation
Full-text PDF (152 kB) Citations (1)
References:
Abstract: The paper explores approaches to identifying hostile insiders of the organization using collusion. The problem of identifying the organized group of information security violators is one of the most complex tasks of ensuring the security of organization. The set of source data for analysis consists of many small samples describing the functionality of the organization's information technologies. This set can be considered as big data. The clustering method is used to reduce the amount of source data that made it possible to use mathematical statistics efficiently, i. e., to identify small samples carrying information about hostile insiders. The difficulty of the task was to lose as little as possible the needed small samples. The conditions have been found where in the series scheme, the probability of identifying insiders using collusion tends to 1.
Keywords: identification of the organized group of hostile insiders, small samples, big data, mathematical statistics.
Funding agency Grant number
Russian Foundation for Basic Research 18-29-03081_мк
The work was partially supported by the Russian Foundation for Basic Research (project 18-29-03081).
Received: 02.06.2020
Document Type: Article
Language: Russian
Citation: A. A. Grusho, M. I. Zabezhailo, D. V. Smirnov, E. E. Timonina, S. Ya. Shorgin, “Mathematical statistics in the task of identifying hostile insiders”, Inform. Primen., 14:3 (2020), 71–75
Citation in format AMSBIB
\Bibitem{GruZabSmi20}
\by A.~A.~Grusho, M.~I.~Zabezhailo, D.~V.~Smirnov, E.~E.~Timonina, S.~Ya.~Shorgin
\paper Mathematical statistics in the task of identifying hostile insiders
\jour Inform. Primen.
\yr 2020
\vol 14
\issue 3
\pages 71--75
\mathnet{http://mi.mathnet.ru/ia681}
\crossref{https://doi.org/10.14357/19922264200310}
Linking options:
  • https://www.mathnet.ru/eng/ia681
  • https://www.mathnet.ru/eng/ia/v14/i3/p71
  • This publication is cited in the following 1 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Информатика и её применения
    Statistics & downloads:
    Abstract page:199
    Full-text PDF :68
    References:15
     
      Contact us:
     Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2024