Comparative analysis of hybrid intelligent models for detecting violations of the critical information infrastructure of the financial sector based on synthetic data

V.A. Abaev; E.S. Budaev; I.I. Khasanov

doi:10.18127/j19997493-202605-03

Comparative analysis of hybrid intelligent models for detecting violations of the critical information infrastructure of the financial sector based on synthetic data

V.A. Abaev¹, E.S. Budaev², I.I. Khasanov³

research article UDC 004.89:[004.056:336] Pages: 19-24 doi.org/10.18127/j19997493-202605-03

^1,2,3 Financial University under the Government of the Russian Federation (Moscow, Russia)

Keywords: Fuzzy neural networks swarm intelligence genetic algorithms synthetic data anomaly detection critical information infrastructure financial sector

Abstract

This article explores the processes of ensuring the cybersecurity of the critical information infrastructure of the financial sector in conditions of uncertainty and a shortage of real data on incidents. The purpose of the study is to develop, implement in software, and compare hybrid intelligent models of network attack detection based on fuzzy logic methods with different optimization mechanisms: adaptive neuro-fuzzy inference system (ANFIS), particle swarm optimization (PSO), and genetic algorithm (GA). The paper uses methods of fuzzy set theory, soft computing, swarm intelligence, evolutionary algorithms, and agent-based modeling to generate synthetic data and perform statistical analysis of classification quality. As a result, a five-parameter synthetic dataset has been designed and implemented to simulate key threat vectors, including DDoS attacks, hidden data leaks, and brute force attacks on a banking API.

Received: 02.09.2026
Approved after review: 09.09.2026
Accepted: 01.10.2026

How to cite

Абаев В.А., Будаев Е.С., Хасанов И.И. Сравнительный анализ гибридных интеллектуальных моделей обнаружения нарушений критической информационной инфраструктуры финансового сектора на синтетических данных // Динамика сложных систем - XXI век. 2026. Т. 20. № 5. С. 19−24. DOI: https://doi.org/10.18127/j19997493-202605-03

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[1] Liao H.J., Lin C.H.R., Lin Y.C. & Tung K.Y. Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications. 2013. 36(1). 16–24. https://doi.org/10.1016/j.jnca.2012.09.004

[2] Buczak A.L. & Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials. 2016. 18(2). 1153–1176. https://doi.org/10.1109/COMST.2015.2494502

[3] Zargar S.T., Joshi J. & Tipper D. A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Communications Surveys & Tutorials. 2013. 15(4). 2046–2069. https://doi.org/10.1109/SURV.2013.031413.00127

[4] Tavallaee M., Bagheri E., Lu W. & Ghorbani A.A. A detailed analysis of the KDD CUP 99 data set. IEEE Symposium on Computational Intelligence for Security and Defense Applications. 2009. 1–6. https://doi.org/10.1109/CISDA.2009.5356528

[5] Moustafa N. & Slay J. UNSW-NB15: A comprehensive data set for network intrusion detection systems. 2015 Military Communications and In-formation Systems Conference (MilCIS). https://doi.org/10.1109/MilCIS.2015.7348942

[6] Ring M., Wunderlich S., Scheuring D., Landes D. & Hotho A. A survey of network-based intrusion detection data sets. Computers & Security. 2019. 86. 147–167. https://doi.org/10.1016/j.cose.2019.06.005

[7] Sharafaldin I., Habibi Lashkari A., & Ghorbani A.A. Toward generating a new intrusion detection dataset and intrusion traffic characterization. Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP). 2018. 108–116. https://doi.org/10.5220/0006639801080116

[8] Zhang G., Wang X., Li R., Song Y., He J. and Lai J. Network Intrusion Detection Based on Conditional Wasserstein Generative Adversarial Network and Cost-Sensitive Stacked Autoencoder. IEEE Access. 2020. V. 8. P. 190431–190447. https://doi.org/10.1109/ACCESS.2020.3031892

[9] Chen S.M. & Chang Y.C. Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. Information Sciences. 2011. 181(24), 5524–5539. https://doi.org/10.1016/j.ins.2010.08.026

[10] Jang J.-S.R. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics. 1993. 23(3). 665–685. https://doi.org/10.1109/21.256541

[11] Kennedy J. & Eberhart R. Particle swarm optimization. Proceedings of ICNN'95 – International Conference on Neural Networks. 1995. 4. 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

[12] Tsang C.H., Kwong S. & Wang H. Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recognition. 2007. 40(9). 2373–2391. https://doi.org/10.1016/j.patcog.2006.12.009

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