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Improving the Quality of the Identification of the Information Security State Based on Sample Segmentation

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Abstract

Increasing the quality indicators for identifying the information security (IS) state of individual segments of cyber-physical systems is related to processing large information arrays. A method for improving quality indicators when solving problems of identifying the IS state is proposed. Its implementation is based on the formation of individual sample segments. Analysis of the properties of these segments makes it possible to select and assign algorithms that have the best quality indicators in the current segment. Segmentation of a data sample is considered. Using real dataset data as an example, experimental values of the quality indicator for the proposed method are given for various classifiers on individual segments and the entire sample.

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REFERENCES

  1. Liu, J., Li, Ya., Song, S., Xing, J., Lan, C., and Zeng, W., Multi-modality multi-task recurrent neural network for online action detection, IEEE Trans. Circuits Syst. Video Technol., 2018, vol. 29, no. 9, pp. 2667–2682. https://doi.org/10.1109/tcsvt.2018.2799968

    Article  Google Scholar 

  2. Salehi, H. and Burgueño, R., Emerging artificial intelligence methods in structural engineering, Eng. Struct., 2018, vol. 171, pp. 170–189. https://doi.org/10.1016/j.engstruct.2018.05.084

    Article  Google Scholar 

  3. Tatarnikova, T.M. and Bogdanov, P.Yu., Intrusion detection in internet of things networks based on machine learning methods, Inf.-Upravlyayushchie Sist., 2021, no. 6, pp. 42–52. https://doi.org/10.31799/1684-8853-2021-6-42-52

  4. Rico-Sulayes, A., Reducing vector space dimensionality in automatic classification for authorship attribution, Rev. Ingeniería Electrónica, Automática y Comunicaciones, 2017, vol. 38, no. 3, pp. 170–199.

    Google Scholar 

  5. Zegzhda, D., Pavlenko, E., and Aleksandrova, E., Modelling artificial immunization processes to counter cyberthreats, Symmetry, 2021, vol. 13, no. 12, p. 2453. https://doi.org/10.3390/sym13122453

    Article  ADS  Google Scholar 

  6. Lebedev, I.S., Adaptive application of machine learning models on separate segments of a data sample in regression and classification problems, Inf.-Upravlyayushchie Sist., 2022, vol. 20, no. 3, pp. 20–30. https://doi.org/10.31799/1684-8853-2022-3-20-30

    Article  Google Scholar 

  7. Atsuya, O. and Genki, Y., Computational mechanics enhanced by deep learning, Comput. Methods Appl. Mech. Eng., 2017, vol. 327, pp. 327–351. https://doi.org/10.1016/j.cma.2017.08.040

    Article  MathSciNet  Google Scholar 

  8. Chao, Yu-W., Vijayanarasimhan, S., Seybold, B., Ross, D.A., Deng, J., and Sukthankar, R., Rethinking the faster R-CNN architecture for temporal action localization, 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Salt Lake City, Utah, 2018, IEEE, 2018, pp. 1130–1139. https://doi.org/10.1109/cvpr.2018.00124

  9. Nguyen, P., Han, B., Liu, T., and Prasad, G., Weakly supervised action localization by sparse temporal pooling network, 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Salt Lake City, 2018, IEEE, 2018, pp. 6752–6761. https://doi.org/10.1109/cvpr.2018.00706

  10. Park, J. and Kim, S., Machine learning-based activity pattern classification using personal PM2.5 exposure information, Int. J. Environ. Res. Public Health, 2020, vol. 17, no. 18, p. 6573. https://doi.org/10.3390/ijerph17186573

    Article  PubMed  PubMed Central  Google Scholar 

  11. Takacs, A., Toledano-Ayala, M., Dominguez-Gonzalez, A., Pastrana-Palma, A., Velazquez, D.T., Ramos, J.M., and Rivas-Araiza, E.A., Descriptor generation and optimization for a specific outdoor environment, IEEE Access, 2020, vol. 8, pp. 52550–52565. https://doi.org/10.1109/access.2020.2975474

    Article  Google Scholar 

  12. Dhanabal, L. and Shantharajah, S.P., A study on NSL-KDD dataset for intrusion detection system based on classification algorithms, Int. J. Adv. Res. Comput. Commun. Eng., 2015, vol. 446, p. 452. https://doi.org/10.17148/IJARCCE.2015.4696

    Article  Google Scholar 

  13. Wang, F. and Gelfand, A.E., Modeling space and space-time directional data using projected Gaussian processes, J. Am. Stat. Assoc., 2014, vol. 109, no. 508, pp. 1565–1580. https://doi.org/10.1080/01621459.2014.934454

    Article  MathSciNet  CAS  Google Scholar 

  14. Lebedev, I.S. and Sukhoparov, M.E., Adaptive learning and integrated use of information flow forecasting methods, Emerging Sci. J., 2023, vol. 7, no. 3, pp. 704–723. https://doi.org/10.28991/esj-2023-07-03-03

    Article  Google Scholar 

  15. Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G., Learning under concept drift: A review, IEEE Trans. Knowl. Data Eng., 2019, vol. 31, pp. 2346–2363. https://doi.org/10.1109/tkde.2018.2876857

    Article  Google Scholar 

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This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. Russian State Hydrometeorological University, 192007, St. Petersburg, Russia

    M. E. Sukhoparov

  2. St. Petersburg Federal Research Center, Russian Academy of Sciences, 199178, St. Petersburg, Russia

    I. S. Lebedev

Authors
  1. M. E. Sukhoparov
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  2. I. S. Lebedev
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Correspondence to I. S. Lebedev.

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Sukhoparov, M.E., Lebedev, I.S. Improving the Quality of the Identification of the Information Security State Based on Sample Segmentation. Aut. Control Comp. Sci. 57, 1071–1075 (2023). https://doi.org/10.3103/S0146411623080321

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