Odarushchenko, O. B.Одарущенко, Олена Борисівна2026-08-032026-08-032025Development of a method for detecting cyber attacks on information systems based on artificial intelligence technologies / S. R. Owaid et. al. Eastern-European Journal of Enterprise Tech¬nologies. 2025. № 3 (9 (135)). Pp. 33–39.https://doi.org/10.15587/1729-4061.2025.329258https://dspace.pdau.edu.ua/handle/123456789/22182The object of this research is arti¬ficial immune systems. The problem addressed in the study is improving the responsiveness of cyberattack detection in information systems while ensuring a predetermined level of convergence, regardless of the num¬ber of destabilizing factors. The sub-ject of the research is the cyberattack detection process. A cyberattack detection method for information systems based on arti-ficial intelligence technologies is pro¬posed. The originality of the method lies in the use of additional enhanced procedures that allow: – initializing the initial popula¬tion of swarm agents and verifying information system parameters using an improved bat algorithm, which minimizes the error of entering incor¬rect data concerning the operational information system of military forces; – performing initial identification of attacks specific to the given infor-mation system using a decision tree; – adapting to the type and dura¬tion of cyberattacks through multi-lev¬el adaptation of the artificial immune system; – conducting initial selection of antibodies for each swarm of the artificial immune system using an improved genetic algorithm; – training general-swarm anti¬bodies using elite-swarm antibodies, thereby enabling deep learning; – replacing unfit individuals for search through antibody population renewal; – performing simultaneous solu¬tion search in multiple directions; – calculating the required amount of computational resources in cases where available resources are insuf¬ficient for the necessary calculations. An example application of the proposed method was conducted for cyberattack detection in an operation¬al military force group. The results demonstrated an average increase in detection accuracy by 16%, an aver-age improvement in responsiveness by 12%, and a high result convergence level of 95.23%encyberattacksdecision treegenetic algorithmdestabilizing factorsmilitary force groupingArticle