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Trust Evaluation Problems in Big Data Analytics

Trust Evaluation Problems in Big Data Analytics This paper considers the problem of trust evaluation in complex computer-aided data analysis. We use a well-known approach that consists of constructing empirical regularities based on measures of use case similarity in the training sample. Trust is approximated by modeling training data with the use of a random sample from an unknown distribution. This approach implements approximate causal analysis and has advantages and disadvantages. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

Trust Evaluation Problems in Big Data Analytics

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Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2022. ISSN 0146-4116, Automatic Control and Computer Sciences, 2022, Vol. 56, No. 8, pp. 847–851. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy.
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411622080077
Publisher site
See Article on Publisher Site

Abstract

This paper considers the problem of trust evaluation in complex computer-aided data analysis. We use a well-known approach that consists of constructing empirical regularities based on measures of use case similarity in the training sample. Trust is approximated by modeling training data with the use of a random sample from an unknown distribution. This approach implements approximate causal analysis and has advantages and disadvantages.

Journal

Automatic Control and Computer Sciences – Springer Journals

Published: Dec 1, 2022

Keywords: information security; intelligent data analysis; causal relationships; and contradictory data

References

  • Key concepts of systemological approach to CPS adaptive information security monitoring
    Poltavtseva, M.; Shelupanov, A.; Bragin, D.; Zegzhda, D.; Alexandrova, E.
  • Modelling artificial immunization processes to counter cyberthreats
    Zegzhda, D.; Pavlenko, E.; Aleksandrova, E.
  • Approaches to modeling the security of cyberphysical systems
    Zegzhda, D.P.; Vasil’ev, Yu.S.; Poltavtseva, M.A.
  • Universal and local constraints in the problem of correction of heuristic algorithms
    Rudakov, K.V.
  • Correct algebras over sets of incorrect (Heuristic) algorithms. I
    Zhuravlev, Yu.I.
  • Correct algebras over sets of incorrect (Heuristic) algorithms. II
    Zhuravlev, Yu.I.
  • Correct algebras over sets of incorrect (Heuristic) algorithms. III
    Zhuravlev, Y.I.
  • A use case analysis of heterogeneous semistructured objects in information security problems
    Zegzhda, P.D.; Poltavtseva, M.A.; Pechenkin, A.I.; Lavrova, D.S.; Zaitseva, E.A.
  • Intelligent data analysis in decision support systems for penetration tests
    Poltavtseva, M.A.; Pechenkin, A.I.
  • DARPA’s explainable artificial intelligence (XAI) program
    Gunning, D.; Aha, D.W.