Moscow, Russian Federation
VAC 5.4.4 Социальная структура, социальные институты и процессы
UDK 004.81 Модели когнитивных процессов
The article introduces a new cognitive reframing approach to anticipating and preventing multiplex threats to critical infrastructure. In the context of constantly evolving threats, the model may increase the effectiveness of incident prevention strategies. It is visualized as a graph with nodes for concepts of cognitive reframing and edges for the connections between them. The model includes weight values that depend on the importance of each concept, as well as additional importance metrics, coefficients, and interactions. By calculating the edge weights, the authors developed a graph that illustrates the interrelationships between the concepts. The model can be applied to various scenarios as it improves cybersecurity, responds to natural disasters, and ensures the smooth operation of various systems. The model takes into account dynamic factors, multiple importance metrics, interactions, and statistical methods, which makes it flexible and adaptive. Extra factors could increase the complexity, accuracy, and adaptability of the current model. Cognitive reframing has good prospects in the field of critical infrastructure while the new model proves to be an effective threat management tool.
cognitive reframing, critical infrastructure, security, threats, weight calculation model, threat prevention, cybersecurity, smooth operation of systems, critical infrastructure management
1. Brumstein Yu. M., Molimonov D. A., Krivenko A. I., Grotskaya A. Y. System analysis of goals, directions and technical solutions for the study of human visual perception and memory processes. Physics and radioelectronics in medicine and ecology – FRAME'2020: Proc. XIV Intern. Sci. Conf., Vladimir-Suzdal, 1–3 Jul 2020. Vladimir: VSU, 2020, 336–341. (In Russ.) https://elibrary.ru/tqnwdi
2. Valeev R. R., Orlov S. P. The organization of information security systems on the basis of the computer decision support system. Nauka i mir, 2018, (6-1): 16–21. (In Russ.) https://elibrary.ru/ucugkd
3. Garifullina L. A., Isavnin A. G. Assessing the relevance and effectiveness of the integration of artificial neural networks in information security systems. Modern Science, 2021, (3-2): 467–472. (In Russ.) https://elibrary.ru/ohqnom
4. Gromov Yu. Yu., Eliseev A. I., Didrikh V. E., Ulanov A. O. Mathematical support monitoring systems reliability and security of network-centric information system. Information & Security, 2015, 18(4): 602–607. (In Russ.) https://elibrary.ru/vadqbn
5. Gubanov V. P., Zakirov I. F. Methods of vulnerability analysis of information systems. Information technologies and computing systems, 2015, (2): 31–39. (In Russ.)
6. Kazmina I. V., Potudinsky A. V., Kryuchkov R. A. Ensuring information security at high-tech enterprises in the military-industrial. Tsifrovaia i otraslevaia ekonomika, 2023, (3): 40–46. (In Russ.) https://elibrary.ru/osiian
7. Kartashev E. N., Krasovskiy V. S. Information security of a modern enterprise engaged in defense-industrial sector. Voprosy zashchity informatsii, 2016, (4): 41–46. (In Russ.) https://elibrary.ru/xehnrp
8. Kurmanbai A. K., Nozirzoda S. S. A new system of information security criteria in information systems. Novaia nauka: Ot idei k rezultatu, 2016, (5-2): 175–178. (In Russ.) https://elibrary.ru/vzgjzn
9. Lavrinenko A. A., Goncharenko V. M. Methods of graph analysis in information security problems. Information technologies and computing systems, 2016, (3): 63–70. (In Russ.)
10. Laptev V. N., Sidelnikov O. V., Sharaj V. A. Application of the method of the inductive forecasting of states for detecting of computer attacks in information-telecommunication systems. Polythematic Online Scientific Journal of Kuban State Agrarian University, 2011, (72): 76–85. (In Russ.) https://elibrary.ru/oiuett
11. Panilov P. A., Kokorev A. V. Evolutionary algorithms for optimizing critical infrastructure security management based on cognitive maps. Informatization and information security of law enforcement agencies, ed. Betskov A. B. Moscow: AM MIA RF, 2024, 232–238. (In Russ.) https://elibrary.ru/bpcwno
12. Panilov P. A., Tsibizova T. Yu., Chernega E. V. Development of an algorithm for managing cognitive functions in intelligent security systems. Izvestiya Tula State University. Technical sciences, 2023, (10): 47–61. (In Russ.) https://doi.org/10.24412/2071-6168-2023-10-47-48
13. Proletarsky A. V., Skvortsova M. A., Terekhov V. I. Hybrid intelligent system for risk assessment based on unstructured data. Neurocomputers: development, application, 2017, (1): 66–74. (In Russ.) https://elibrary.ru/yhwrez
14. Skrypnikov A. V., Chernyshova E. V., Yatsenko Yu. I. A new algorithm for automatic identification of a priori features of an information security system. Theory and practice of modern science: Proc. XVII Intern. Sci.-Prac. Conf., Moscow, 8–9 Apr 2015. Moscow: Institut strategicheskikh issledovanii, 2015, 65–74. (In Russ.) https://elibrary.ru/tqfvoj
15. Trofimov O. V., Saakyan A. G. Digitalization and the problems of ensuring information security in the military-industrial companies of the Russian Federation. Creative Economy, 2023, 17(9): 3331–3344. (In Russ.) https://doi.org/10.18334/ce.17.9.119149
16. Tsibizova T. Yu., Panilov P. A., Kocheshkov M. A. Monitoring the security of the information security system of the critical information infrastructure based on cognitive modeling. Izvestiya Tula State University. Technical sciences, 2023, (6): 33–41. (In Russ.) https://doi.org/10.24412/2071-6168-2023-6-33-41
17. Chen J., Zou Y., Wen Y. Blockchain-based internet of things and edge computing for resilient critical infrastructure. IEEE Network, 2019, 33(1): 156–165.
18. Panilov P., Tsibizova T., Voskresensky G. Methodology of expert-agent cognitive modeling for preventing impact on critical information infrastructure. High-performance computing systems and technologies in scientific research, automation of control and production: Proc. 13 Intern. Conf., Barnaul, 19–20 May 2023. Cham: Springer, 2024, 276–287. https://doi.org/10.1007/978-3-031-51057-1_21
19. Wang Q., Guo C., Wu H. A deep learning-based cybersecurity risk assessment approach for smart factories. IEEE Transactions on Industrial Informatics, 2021, 17(3): 1783–1793.
