INFORMATION LEAKAGE DETECTION ALGORITHM BASED ON CHECKS OF STATISTICAL HYPOTHESES

DOI: 10.31673/2412-4338.2024.019505

Abstract

The successful implementation of modern research requires the use of a wide variety of methods that can be used to solve problems in the presence of uncertainties. The difference between Bayesian data analysis methods is that they do not require the availability of significant volumes of data on which the necessary models can be built for their further use. In fact, this method can be based on short samples, on expert assessments, individual measurements, which is what justifies its use for detecting random radio monitoring signals. An algorithm for detection and recognition of signals of means of covertly obtaining information based on the verification of statistical hypotheses is proposed. Mathematical modeling of two cases where the first a priori distribution of the signal parameter is uniform and the second case when the a priori distribution of the parameter is a normal distribution is carried out. Mathematical calculations showed the possibility of using Bayes' theorem to build an algorithm for detecting and recognizing signals of means of tacitly obtaining information. It is proved that if the sample mean has a normal distribution and a known variance, and the probability has a normal distribution, then the posterior distribution for the probability is also normal. The calculations proved the advantages of the second case for which the variance of the posterior distribution decreased from the value of 2.0 to 0.25, which led to a significant reduction in the uncertainty of this distribution due to the data obtained after radio monitoring. This proved the possibility of using Bayes' theorem to detect and recognize signals of means of tacitly obtaining information and the adequacy of the proposed algorithm.

Keywords: Bayes theorem, personal data, personal information, hypothesis, random signal, method, false information, clustering.

References
1. Zamrii I., Sobchuk V., Laptiev O., Savchenko V., Shkapa V., Kovalenko V. and Kotok V. Fractal Functions and Their Application to Source Data Coding. ARPN Journal of Engineering and Applied Sciences. Vol. 17, No. 4, 2022. pp. 424-435.
2. Tetiana Laptieva. Algorithm for determining the degree of existence of unreliable information in the conditions of information conflict. Cyber security: education, science, technology. No. 2 (14), 2021, p. 15-25.
3. V. S. Nakonechnyi, O. V. Barabash, T. O. Laptieva, and A. V. Mishchenko. Improvement of the method of detection and clustering of sources of false information. Scientific technologies. Information technologies, cyber security. Volume 54 No. 4 (2022) pp. 105 - 111.
4. Zhigalkevich Zh.M. Clusters of interacting enterprises and their classification. Bulletin of ONU named after I.I. Mechnikova, 2014. T. 19, Issue 2/3. with. 98-101.
5. Lapteva T.O., Lukova-Chuiko N.V., Sobchuk A.V. Study of the main threats and assessment of the security of information systems. Math. Information Technology. Education. 2022: a collection of theses add. member. XI International science and practice conference, June 3–5, 2022. Lutsk–Svityaz: Lesya Ukrainka SNU, 2022. p. 101-103
6. Ryabiy M. O. Khatyan O. A., Bagatskyi C. P. A model for detecting PR influence through publications on the Internet mass media. Informational security. 2015. Vol. 21, No. 2. p. 131-1.39.
7. Polishchuk Y. Ya., Hnatiuk S. O., Seilona P. A. Mac media as a channel of manipulative influence on society. Informational security. 2015. Vol. 21, Part 3. p. 301-308.
8. V. Theocharis, W. Lowe, J. W. van Deth, G. Garcla-Albacete. Using Twitter to mobilize protest action: Online mobilization patterns and action repertoires in the Occupy Wall Street, Indignados, and Aganaktismenoi movements. Information, Communication & Society. 2015. 18. pp. 202-220.
9. Butko, T., Prokhorchenko, A., Muzykin, M. An improved method of determining the schemes of locomotive circulation with regard to the technological peculiarities of railcar traffic. Eastern-European Journal of Enterprise Technologies. 2016. 5(3 (83)), pp. 47–55.
10. Molodetska K. V. Approach to identifying organizational features of information operations in social Internet services. Priority areas of development of telecommunication systems and special purpose networks. Application of divisions, complexes, means of communication and automation in ATO: collection. the mother IX science-practice conference, November 25 2016. Kyiv: VITI. 2016. P. 130-131.
11. Faraz A. A comparison of text categorization methods. International Journal on Natural Language Computing. 2016.-5(1). years 31-44.
12. Laptev O.A., Babenko R.V., Pravdyviy A.M., Zozulya S.A., Stefurak O.R. An improved technique for choosing the sequence of priorities for servicing information flows. Scientific and practical magazine "Zvyazok". K.: DUT, 2020. No. 4 (146), pp. 27-31.
13. O. Svynchuk, O. Barabash, J. Nikodem, R. Kochan, O. Laptiev. Image compression using fractal functions. Fractal and Fractional, 2021, 5(2), 31.pp.1-14. DOI:10.3390/fractalfract5020031 - 14 Apr 2021
14. Zamrii I., Sobchuk V., Laptiev O., Savchenko V., Shkapa V., Kovalenko V. and Kotok V. Fractal Functions and Their Application to Source Data Coding. ARPN Journal of Engineering and Applied Sciences. Vol. 17, No. 4, 2022. pp. 424-435
15. Serhii Yevseiev, Volodymir Ponomarenko, Oleksandr Laptiev, Oleksandr Milov and others/ Synergy of building cybersecurity systems. Kharkiv. Publisher PC TECHNOLOGY CENTER. 2021 – 188 p.
13. Lukova-Chuiko N., Gerasimenko O., Tolyupa S., ...Laptieva T., Laptiyev O. The method of detecting radio signals by estimating the parameters of signals of rotary Gaussian propagation. 2021 IEEE 3rd International Conference on Advanced Trends in Information Theory, ATIT 2021 - Proceedings, 2021, p. 67–70.
14. Vlasyk H., Zamriy I., Shkapa V., ... Kalynyuk A., Lapteva T. A method of solving problems of optimal restoration of telecommunication signals. 2021 IEEE 3rd International Conference on Advanced Trends in Information Theory, ATIT 2021 - Proceedings, 2021, p. 71–75.
15. Bidyuk P.I. Bayesian data analysis: monograph / P.I. Bidyuk, I.O. Kalinina, O.P. Gozhiy – Kherson: Book. Vyshemyrsky V.S., 2021. – 208 p.
16. Serhii Laptiev. An improved method of protecting personal data from attacks using social engineering algorithms. Electronic specialized scientific publication "Cybersecurity: education, science, technology", 4(16), 2022. P. 45–62.
17. S. Laptiev, S. Tolupa. The methodology for evaluating the functional stability of the protection system of special networks. Scientific technologies. Information technologies, cyber security. Volume 55 No. 3 (2022) C.178 – 183.