Development of method for the identification of hybrid challenges and threats in the national security management system
Creators
- 1. Taras Shevchenko Kyiv National University
- 2. Research Institute of Military Intelligence
- 3. The National University of Defense of Ukraine named after Ivan Chernyakhovskyi
- 4. Central Scientifically-Research Institute of Armaments and Military Equipments of the Armed Forces of Ukraine
- 5. National Technical University «Kharkiv Polytechnic Institute»
- 6. Poltava State Agrarian University
- 7. Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty
Description
As a result of Russian aggression against Ukraine, some fundamental theses regarding the nature of hybrid military operations will require clarification and even revision. First of all, this refers to the widespread perception of the asymmetric nature of hybrid threats as those used by a weaker opponent against a party with significantly greater military, technological and human potential. This, in turn, requires the use of modern and proven mathematical apparatus, which is capable of processing a large array of various types of data in a short period of time with a given reliability of making management decisions. The object of research is the system of strategic management of national security. The subject of the research is the method of detection and identification of hybrid challenges and threats in the national security management system. In the research, the method of detection and identification of hybrid challenges and threats in the national security management system was developed. The novelty of the research:
– a destructive effect on the system of national security management by adding an appropriate correction factor;
– the use of an improved procedure of deep learning of the database of the system of detection and identification of hybrid challenges and threats to the national security of the state;
– a mechanism for resolving conflicting cases of classification is used due to additional training, adaptation of detectors to the type and intensity of the hybrid challenge and threat to the national security of the state;
– the procedure for automatically calculating the detector activation threshold and the universality of the structure of their representation due to the hierarchy and flexibility for the available hardware resources of the detection and identification system.
It is advisable to implement the specified method in algorithmic and software while studying the state of the national security system.
Files
Minimization of ships' passing path in the field of risks.pdf
Files
(648.9 kB)
Name | Size | Download all |
---|---|---|
md5:1f44ba6d58cc868dbe21ff2a57d9b37e
|
648.9 kB | Preview Download |
Additional details
References
- Shyshatskyi, A. V., Bashkirov, O. M., Kostina, O. M. (2015). Rozvitok іntegrovanikh sistem zv'iazku ta peredachі danikh dlia potreb Zbroinikh Sil. Ozbroennia ta vіiskova tekhnіka, 1 (5), 35–40.
- Timchuk, S. (2017). Methods of Complex Data Processing from Technical Means of Monitoring. Path of Science, 3 (3), 4.1–4.9. doi: http://doi.org/10.22178/pos.20-4
- Sokolov, K. O., Hudyma, O. P., Tkachenko, V. A., Shyiatyi, O. B. (2015). Main directions of creation of IT infrastructure of the Ministry of Defense of Ukraine. Zbirnyk naukovykh prats Tsentru voienno-stratehichnykh doslidzhen, 3 (6), 26–30.
- Shevchenko, D. G. (2020). The set of indicators of the cyber security system in information and telecommunication networks of the armed forces of Ukraine. Suchasnі іnformatcіinі tekhnologіi u sferі bezpeki ta oboroni, 38 (2), 57‒62. doi: https://doi.org/10.33099/2311-7249/2020-38-2-57-62
- Makarenko, S. I. (2017). Perspektivy i problemnye voprosy razvitiia setei sviazi spetcialnogo naznacheniia. Sistemy upravleniia, sviazi i bezopasnosti, 2, 18–68. Available at: http://sccs.intelgr.com/archive/2017-02/02-Makarenko.pdf
- Zuiev, P., Zhyvotovskyi, R., Zvieriev, O., Hatsenko, S., Kuprii, V., Nakonechnyi, O. (2020). Development of complex methodology of processing heterogeneous data in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (9 (106)), 14‒23. doi: http://doi.org/10.15587/1729-4061.2020.208554
- Brownlee, J. (2011). Clever algorithms: nature-inspired programming recipes. LuLu, 441.
- Gorokhovatsky, V., Stiahlyk, N., Tsarevska, V. (2021). Combination method of accelerated metric data search in image classification problems. Advanced Information Systems, 5 (3), 5–12. doi: http://doi.org/10.20998/2522-9052.2021.3.01
- Meleshko, Y., Drieiev, O., Drieieva, H. (2020). Method of identification bot profiles based on neural networks in recommendation systems. Advanced Information Systems, 4 (2), 24–28. doi: https://doi.org/10.20998/2522-9052.2020.2.05
- Rybak, V. A., Shokr, A. (2016). Analysis and comparison of existing decision support technology. System analysis and applied information science, 3, 12–18.