Development of a method for detecting cyber attacks on information systems based on artificial intelligence technologies

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.329258

Keywords:

cyberattacks, decision tree, genetic algorithm, destabilizing factors, military force grouping

Abstract

The object of this research is artificial 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 number of destabilizing factors. The subject of the research is the cyberattack detection process.

A cyberattack detection method for information systems based on artificial intelligence technologies is proposed. The originality of the method lies in the use of additional enhanced procedures that allow:

– initializing the initial population of swarm agents and verifying information system parameters using an improved bat algorithm, which minimizes the error of entering incorrect data concerning the operational information system of military forces;

– performing initial identification of attacks specific to the given information system using a decision tree;

– adapting to the type and duration of cyberattacks through multi-level 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 antibodies using elite-swarm antibodies, thereby enabling deep learning;

– replacing unfit individuals for search through antibody population renewal;

– performing simultaneous solution search in multiple directions;

– calculating the required amount of computational resources in cases where available resources are insufficient for the necessary calculations.

An example application of the proposed method was conducted for cyberattack detection in an operational military force group. The results demonstrated an average increase in detection accuracy by 16 %, an average improvement in responsiveness by 12 %, and a high result convergence level of 95.23 %

Author Biographies

Salman Rasheed Owaid, Al Taff University College

PhD, Assosiate Professor, Lecturer of Department

Department of Computer Engineering

Andrii Shyshatskyi, State University “Kyiv Aviation Institute”

Doctor of Technical Sciences, Senior Researcher, Professor

Department of Intelligent Cybernetic Systems

Svitlana Kashkevich, State University “Kyiv Aviation Institute”

Senior Lecturer

Department of Intelligent Cybernetic Systems

Vitalii Stryhun, State Scientific Research Institute of Armament and Military Equipment Testing and Certification

Senior Researcher, Senior Test Engineer

Research Laboratory

Ganna Plekhova, Kharkiv National Automobile and Highway University

PhD, Associate Professor, Head of Department

Department of Computer Science and Information Systems

Elena Odarushchenko, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Andrii Hrymud, National Defense University of Ukraine

PhD, Listener

Institute of Information and Communication Technologies and Cyber Defense

Olena Shaposhnikova, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Computer Science and Information Systems

Serhii Petruk, Central Scientific Research Institute of Armament and Military Equipment of the Armed Forces of Ukraine

PhD, Senior Reserher, Deputy Chief of Research Department

Hennadii Miahkykh, National Defense University of Ukraine

Adjunct

Institute of Information and Communication Technologies and Cyber Defense

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Development of a method for detecting cyber attacks on information systems based on artificial intelligence technologies

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Published

2025-06-25

How to Cite

Owaid, S. R., Shyshatskyi, A., Kashkevich, S., Stryhun, V., Plekhova, G., Odarushchenko, E., Hrymud, A., Shaposhnikova, O., Petruk, S., & Miahkykh, H. (2025). Development of a method for detecting cyber attacks on information systems based on artificial intelligence technologies. Eastern-European Journal of Enterprise Technologies, 3(9 (135), 33–39. https://doi.org/10.15587/1729-4061.2025.329258

Issue

Vol. 3 No. 9 (135) (2025): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2025 Salman Rasheed Owaid, Andrii Shyshatskyi, Svitlana Kashkevich, Vitalii Stryhun, Ganna Plekhova, Elena Odarushchenko, Andrii Hrymud, Olena Shaposhnikova, Serhii Petruk, Hennadii Miahkykh

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