Intelligent cyberrisk management model for critical information infrastructure in the financial sector based on spiking neural networks

Aim. This work aimed to develop a cyberrisk management model for the critical information infrastructure of the financial sector based on spiking neural networks (SNNs), focused on improving the validity and efficiency of decision­making when detecting network traffic anomalies.

Objectives. To analyze existing approaches to information security incident management in financial organizations; to develop the structure of an intelligent decision support system for detecting network attacks; to identify informative features of network activity and methods for their representation in spiking form; and to perform an experimental evaluation of the proposed approach’s effectiveness within the security management loop.

Methods. The research applies machine learning methods and spiking neural networks. Network event processing is implemented using various SNN architectures, including convolutional and recurrent models. Input data representation is based on converting network traffic parameters into spike trains using probabilistic and temporal encoding methods. Standard classification metrics are used to evaluate the results; additionally, the analysis examines how the obtained values affect the quality of decisions made.

Results. The structure of an intelligent system is developed, which can be integrated into the information security management loop of a financial organization. The system uses specialized spiking neural network models to analyze different types of network threats. Experiments show that using SNNs increases attack detection accuracy and reduces the number of false positives. Consequently, operator workload is reduced, and the efficiency of response processes is improved.

Conclusion. The obtained results demonstrate the feasibility of using spiking neural networks for cybersecurity management tasks in financial organizations. The proposed approach can be integrated into decision support systems, where it helps increase the resilience of critical information infrastructure and mitigate the consequences of cyberthreats.

1. Rahman M., Al Shakil S., Mustakim M.R. A survey on intrusion detection systems in IoT networks. Cyber Security and Applications. 2025;3:100082. https://doi.org/10.1016/j.csa.2024.100082

2. Hozouri A., Mirzaei A., Effatparvar M. A comprehensive survey on intrusion detection systems with advances in machine learning, deep learning and emerging cybersecurity challenges. Discover Artificial Intelligence. 2025;5(1):314. https://doi.org/10.1007/s44163­025­00578­1

3. Zhang Y., Muniyandi R. C., Qamar F. A review of deep learning applications in intrusion detection systems: Overcoming challenges in spatiotemporal feature extraction and data imbalance. Applied Sciences. 2025;15(3):1552. https://doi.org/10.3390/app15031552

4. Lampe B., Meng W. A survey of deep learning­based intrusion detection in automotive applications. Expert Systems with Applications. 2023;221:119771. https://doi.org/10.1016/j.eswa.2023.119771

5. Xu Z., Wu Y., Wang S., et al. Deep learning­based intrusion detection systems: A survey. Journal of the ACM. 2025;1(1):1. https://doi.org/10.48550/arXiv.2504.07839

6. Arnob A.K.B., Chowdhury R.R., Chaiti N.A., Saha S., Roy A.A Comprehensive systematic review of intrusion detection systems: Emerging techniques, challenges, and future research directions. Journal of Edge Computing. 2025;4(1):73­104. https://doi.org/10.55056/jec.885

7. Mohammed A.A.A. Improving intrusion detection systems by using deep learning methods on time series data. Engineering, Technology & Applied Science Research. 2025;15(1): 19267­19272. https://doi.org/10.48084/etasr.9417

8. Shone N., Ngoc T.N., Phai V.D., Shi Q. A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence. 2018;2(1):41­50. https://doi.org/10.1109/TETCI.2017.2772792

9. Tang T.A., Mhamdi L., McLernon D., Zaidi S.A., Ghogho M. DeepIDS: Deep learning approach for intrusion detection in software defined networking. Electronics. 2020;9(9):1533. https://doi.org/10.3390/electronics9091533

10. Yin C., Zhu Y., Fei J., He X. A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access. 2017;5:21954­21961. https://doi.org/10.1109/ACCESS.2017.2762418

11. Kotenko I.V. Artificial intelligence for cyber security: A new stage of confrontation in cyberspace. Iskusstvennyi intellekt i prinyatie reshenii = Artificial Intelligence and Decision Making. 2024;(1):3­19. (in Russ.).

12. Ichetovkin E.A., Kotenko I.V. Models and algorithms for protecting intrusion detection systems from attacks on machine learning components. Computational Nanotechnology. 2025;12(1):17­25. (In Russ.). https://doi.org/10.33693/2313­223X­2025­12­1­17­25

13. Novikova E.S., Fedorchenko E.V., Kotenko I.V., Kholod I.I. Analytical review of intelligent intrusion detection systems based on federated learning: Advantages and open challenges. Informatika i avtomatizatsiya = Informatics and Automation. 2023;22(5):1034­1082. (In Russ.). https://doi.org/10.15622/ia.22.5.4

14. Trufanov V.N., Ogarok A.L., Nesterov S.G. A research on network intrusion detection systems using machine learning techniques. Informatizatsiya i svyaz’ = Informatization and Communication. 2023;(4):59­72. https://doi.org/10.34219/2078­8320­2023­14­4­59­72

15. Ichetovkin E.A. Investigating the resistance of intrusion detection systems with machine learning components to adversarial attacks. Vestnik Astrakhanskogo gosudarstvennogo tekh- nicheskogo universiteta. Seriya: Upravlenie, vychislitel’naya tekhnika i informatika = Vestnik of Astrakhan State Technical University. Series: Management, Computer Science and Informatics. 2025;(2):76­87. (In Russ.). https://doi.org/10.24143/2072­9502­2025­2­76­87

16. Moustafa N., Slay J. UNSW­NB15: A comprehensive data set for network intrusion detection systems (UNSW­NB15 network data set). In: Military communications and information systems conf. (MilCIS). (Canberra, November 10­12, 2015). New York, NY: IEEE; 2015:1­6. https://doi.org/10.1109/MilCIS.2015.7348942

17. Tavanaei A., Ghodrati M., Kheradpisheh S.R., Masquelier T., Maida A. Deep learning in spiking neural networks. Neural Networks. 2019;111:47­63. https://doi.org/10.1016/j.neunet.2018.12.002

18. Lin T.­Y., Goyal P., Girshick R., He K., Dollár P. Focal loss for dense object detection. In: Proc. IEEE Int. conf. on computer vision (ICCV). (Venice, October 22­29, 2017). New York, NY: IEEE; 2017:2980­2988. https://doi.org/10.1109/ICCV.2017.324

19. Paszke A., Gross S., Massa F., et al. PyTorch: An imperative style, high­performance deep learning library. In: Proc. 33rd Int. conf. on neural information processing systems (NeurIPS 2019). (Vancouver, BC, December 8­14, 2019). New York, NY: ACM; 2019:8024­8035. URL:https://proceedings.neurips.cc/paper_files/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740Paper.pdf (accessed on 15.04.2026).

20. Eshraghian J.K., Ward M., Neftci E.O., et al. Training spiking neural networks using lessons from deep learning. Proceedings of the IEEE. 2023;111(9):1016­1054. https://doi.org/10.1109/JPROC.2023.3308088

21. McKinney W. Data structures for statistical computing in Python. In: Proc. 9th Python in science conf. (SciPy 2010). (Austin, TX, June 28 – July 03, 2010). Austin, TX: SciPy; 2010:51­56. https://doi.org/10.25080/Majora­92bf1922­00a