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Zabezhailo Mikhail Ivanovich

Publications in Math-Net.Ru

  1. Classification of small sets of data of large dimension

    Inform. Primen., 19:3 (2025),  67–72
  2. Machine learning and trust in classification results

    Inform. Primen., 19:2 (2025),  63–68
  3. Analysis of failures by indirect features

    Sistemy i Sredstva Inform., 35:1 (2025),  59–70
  4. Identification of cause-and-effect relationships when covering causes

    Inform. Primen., 18:2 (2024),  54–59
  5. Logic of deception in machine learning

    Inform. Primen., 18:1 (2024),  78–83
  6. The possibility to use artificial intelligence methods in predicting the outcomes of neurosurgical operations

    Artificial Intelligence and Decision Making, 2024, no. 2,  37–52
  7. XXI National Conference on Artificial Intelligence

    Artificial Intelligence and Decision Making, 2024, no. 1,  129–141
  8. To the problem of identifying failures in the information technology infrastructure by monitoring and analyzing indirect data

    Sistemy i Sredstva Inform., 34:3 (2024),  14–22
  9. Functional safety monitoring of large service systems

    Sistemy i Sredstva Inform., 34:3 (2024),  3–13
  10. Classification by cause-and-effect relationships

    Inform. Primen., 17:3 (2023),  71–75
  11. Complex cause-and-effect relationships

    Inform. Primen., 17:2 (2023),  84–89
  12. Causal relationships in classification problems

    Inform. Primen., 17:1 (2023),  43–49
  13. Automata models of fault propagation and self-healing

    Sistemy i Sredstva Inform., 33:4 (2023),  28–37
  14. Some challenges of critical infrastructure information security monitoring

    Sistemy i Sredstva Inform., 33:3 (2023),  108–116
  15. Cause-and-effect relationships in analysis of unobservable process properties

    Sistemy i Sredstva Inform., 33:2 (2023),  71–78
  16. Classification problem in conditions of distorted cause-and-effect relationships

    Sistemy i Sredstva Inform., 33:1 (2023),  59–67
  17. About the secure architecture of a microservice-based computing system

    Inform. Primen., 16:4 (2022),  87–92
  18. Cause-and-effect chain analysis

    Inform. Primen., 16:2 (2022),  68–74
  19. Detection of distribution drift

    Sistemy i Sredstva Inform., 32:4 (2022),  14–20
  20. Some approaches to network DLP analysis

    Sistemy i Sredstva Inform., 32:2 (2022),  72–80
  21. Search of anomalies in big data

    Sistemy i Sredstva Inform., 32:1 (2022),  160–167
  22. Statistics and clusters for detection of anomalous insertions in Big Data environment

    Inform. Primen., 15:4 (2021),  79–86
  23. Remote monitoring of workflows

    Inform. Primen., 15:3 (2021),  2–8
  24. Intelligent analysis of Big Data extendible collections under the limits of process-real time

    Inform. Primen., 15:2 (2021),  36–43
  25. On the complexity of characteristic function sets for correct diagnostic problem solving

    Artificial Intelligence and Decision Making, 2021, no. 2,  44–54
  26. Hidden impact without malicious code

    Sistemy i Sredstva Inform., 31:2 (2021),  4–15
  27. Support for solving diagnostic type problems

    Sistemy i Sredstva Inform., 31:1 (2021),  69–81
  28. On probabilistic estimates of the validity of empirical conclusions

    Inform. Primen., 14:4 (2020),  3–8
  29. Mathematical statistics in the task of identifying hostile insiders

    Inform. Primen., 14:3 (2020),  71–75
  30. Methods of finding the causes of information technology failures by means of metadata

    Inform. Primen., 14:2 (2020),  33–39
  31. On causal representativeness of training samples of precedents in diagnostic type tasks

    Inform. Primen., 14:1 (2020),  80–86
  32. To the reliability of medical diagnosis based on empirical data

    Artificial Intelligence and Decision Making, 2020, no. 4,  3–13
  33. Concepts forming on the basis of small samples

    Inform. Primen., 13:4 (2019),  81–84
  34. Architectural decisions in the problem of identification of fraud in the analysis of information flows in digital economy

    Inform. Primen., 13:2 (2019),  22–28
  35. Search of empirical causes of failures and errors in computer systems and networks using metadata

    Sistemy i Sredstva Inform., 29:4 (2019),  28–38
  36. Parametrization in applied problems of search of empirical reasons

    Inform. Primen., 12:3 (2018),  62–66
  37. On some possibilities of resource management for organizing active counteraction to computer attacks

    Inform. Primen., 12:1 (2018),  62–70
  38. Information security on the basis of meta data in enterprise application integration architecture of information systems

    Sistemy i Sredstva Inform., 28:2 (2018),  34–41
  39. About the analysis of erratic statuses in the distributed computing systems

    Sistemy i Sredstva Inform., 28:1 (2018),  99–109
  40. The model of the set of information spaces in the problem of insider detection

    Inform. Primen., 11:4 (2017),  65–69
  41. About complex authentication

    Sistemy i Sredstva Inform., 27:3 (2017),  4–11
  42. Erroneous states classification in dictributed computing systems and sources of their occurence

    Sistemy i Sredstva Inform., 27:2 (2017),  29–40
  43. On the advanced procedure to reduce calculation of Galois closures

    Inform. Primen., 10:4 (2016),  96–104
  44. Integration of statistical and deterministic methods for analysis of information security

    Inform. Primen., 10:3 (2016),  2–8
  45. Secure automatic reconfiguration of cloudy computing

    Sistemy i Sredstva Inform., 26:3 (2016),  83–92
  46. Information flow monitoring and control in the cloud computing environment

    Inform. Primen., 9:4 (2015),  91–97
  47. To the computational complexity of hypotheses generation in JSM-method. Part II

    Artificial Intelligence and Decision Making, 2015, no. 2,  3–17
  48. To the computational complexity of hypotheses generation in JSM-method. Part I

    Artificial Intelligence and Decision Making, 2015, no. 1,  3–17
  49. Architecture of the stand for the pilot study of models, algorithms, and solutions of information security in cloud computing environments

    Sistemy i Sredstva Inform., 25:4 (2015),  65–77
  50. Some capabilities of enumeration control in the DSM method. Part II

    Artificial Intelligence and Decision Making, 2014, no. 3,  3–20
  51. Some capabilities of enumeration control in the DSM method. Part I

    Artificial Intelligence and Decision Making, 2014, no. 2,  3–18
  52. On the problem of subsequences inclusion into the data packages headers

    Sistemy i Sredstva Inform., 23:1 (2013),  58–68

  53. Foreword of the program committee of the conference “Mathematical pattern recognition methods”

    Avtomat. i Telemekh., 2022, no. 10,  3–8
  54. In memory of Konstantin Vladimirovich Rudakov (21.06.1954 – 10.07.2021)

    Computer Research and Modeling, 13:4 (2021),  675–676

© Steklov Math. Inst. of RAS, 2025