SOFTWARE IMPLEMENTATION OF THE PROBLEM OF OPTIMIZING THE CHOICE OF INFORMATION PROTECTION MEANS BASED ON AN EVOLUTIONARY ALGORITHM

Authors

DOI:

https://doi.org/10.28925/2663-4023.2025.27.751

Keywords:

information security; evolutionary algorithms; multi-criteria optimization; NSGA-II; Pareto-optimality; university computer network; selection of information protection tools; modeling.

Abstract

A software implementation of the problem of multi-criteria optimization of the selection of information protection means (IPS) for a university computer network has been developed based on the evolutionary algorithm NSGA-II. A cybernetic model for selecting the optimal set of IPS has been considered taking into account a set of criteria, including the cost of implementation, the level of reliability, and coverage of current threats. Optimization objective functions have been determined, each of which is a function of discrete variables described in the literature and characterizes possible IPS configurations. In the software implementation of NSGA-II, an algorithm for generating the initial population of solutions with crossover, mutation, and selection mechanisms was implemented, which provides an effective search for Pareto-optimal configurations, and computational experiments were also conducted to demonstrate the influence of model parameters on the optimization result visualized on graphs. A Pareto front was obtained, which visualizes the trade-offs between cost, reliability, and level of protection. The results obtained during the study generally confirm that the use of the evolutionary algorithm NSGA-II allows achieving balanced solutions when designing an information security system for a university network. The software implementation of multi-criteria optimization of the ISI parameters for a university network, presented in the article, is implemented in the Python language using specialized optimization and data analysis libraries, ensuring the reproducibility of computational experiments and the ability to adapt the algorithm for various scenarios of protecting distributed computing systems.

Downloads

Download data is not yet available.

References

Lakhno, V., Maliukov, V., Komarova, L., Kasatkin, D., Osypova, T., & Chasnovskyi, Y. (2022). Optimization of information protection means placement based on the application of genetic algorithm. Electronic professional scientific publication “Cybersecurity: education, science, technology”, 1(17), 6–20. https://doi.org/10.28925/2663-4023.2022.17.620

Androschuk, O. S., & Kudin, A. M. (2012). Multi-criteria model for choosing the architecture of a fuzzy logical inference system when analyzing information security risks in cloud computing and other complex systems. Artificial Intelligence, 529–534.

Yezhova, L. F., Skachek, L. M., & Khoroshko, V. O. (2013). Multi-criteria optimization of information security systems. Modern Special Technology, (1), 108–114.

Zhdanova, Yu., Shevchenko, S., Spasitelyeva, S., & Sokulsky, O. (2024). Decision-making based on linear optimization in the process of information security risk management. Electronic professional scientific publication “Cybersecurity: education, science, technology”, 1(25), 330–343. https://doi.org/10.28925/2663-4023.2024.25.330343

Kornienko, B. Ya., & Galata, L. P. (2019). Optimization of the corporate network information protection system. Mathematical and computer modeling. Series: Technical Sciences, 56–62. https://doi.org/10.32626/2308-5916.2019-19.56-62

Junior, M. A. B., de Lima Neto, F. B., & Marwala, T. (2012, June). Optimizing risk management using NSGA-II. 2012 IEEE Congress on Evolutionary Computation, 1–8.

Tamimi, A., Naidu, D.S., & Kavianpour, S. (2015). An Intrusion detection system based on NSGA-II Algorithm. 2015 Fourth International Conference on Cyber Security, Cyber Warfare, and Digital Forensic (CyberSec), 58-61.

Verma, S., Pant, M., & Snasel, V. (2021). A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems. IEEE Access, 9, 57757–57791.

De Almeida, E. M., & Asada, E. N. (2015). NSGA-II applied to the multi-objective Distribution System Expansion Planning problem. 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP), 1–6.

Fang, X., Wang, W., He, L., Huang, Z., Liu, Y., & Zhang, L. (2018). Research on Improved NSGA-II Algorithm and Its Application in Emergency Management. Mathematical Problems in Engineering, 2018(1), 1306341.

Lakhno, V., Alimseitova, Z., Kalaman, Y., Kryvoruchko, O., Desiatko, A., & Kaminskyi, S. (2023). Development of an information security system based on modeling distributed computer network vulnerability indicators of an informatization object. International Journal of Electronics and Telecommunications, 69. http://dx.doi.org/10.24425/ijet.2023.146495

Hulak, H. M., Zhiltsov, O. B., Kyrychok, R. V., Korshun, N. V., & Skladannyi, P. M. (2024). Information and cyber security of the enterprise. Textbook. Lviv: Publisher Marchenko T. V.

Downloads


Abstract views: 11

Published

2025-03-27

How to Cite

Lakhno , V., Kryvoruchko, O., & Kalaman, Y. (2025). SOFTWARE IMPLEMENTATION OF THE PROBLEM OF OPTIMIZING THE CHOICE OF INFORMATION PROTECTION MEANS BASED ON AN EVOLUTIONARY ALGORITHM. Electronic Professional Scientific Journal «Cybersecurity: Education, Science, Technique», 3(27), 257–268. https://doi.org/10.28925/2663-4023.2025.27.751

Most read articles by the same author(s)

1 2 > >>