Tel: +998977838464
Search

No. 1 (2026)
Marketing

Articles

INTELLIGENT INTRUSION DETECTION: LEVERAGING REINFORCEMENT LEARNING FOR ECONOMIC SECURITY AGAINST

Published 2026-01-31

Authors
DDOS THREATS a , Bekov Sanjar Nigmandjanovich b
Affiliations
a N/A
b Independent Researcher at Tashkent International University
PDF Google Scholar Google Scholar

Abstract

Distributed Denial -of-Service (DDoS) attacks are a persistent threat to digital services and pose a substantial risk to economic stability. This risk is especially notable within financial systems. Conventional intrusion detection systems rely on static ru les or supervised learning models. Both struggle to adapt to rapidly evolving attack strategies. This paper examines the use of reinforcement learning (RL) for intelligent intrusion detection. It emphasizes RL's ability to enable adaptive, real -time decisi ons in adversarial environments. By viewing intrusion detection as a series of decisions, RL-based systems learn optimal mitigation strategies. They do this through ongoing interaction with network environments and feedback -driven reward mechanisms. The analysis brings together recent empirical studies and experimental findings. It shows that RL -based intrusion detection systems achieve high detection accuracy, faster response times, and greater resilience to evolving DDoS threats. These results suggest that RL not only improves technical detection but also boosts economic security. RL minimizes service downtime, reduces operational losses, and supports the provision of continuous digital financial services.

Keywords

reinforcement Learning, Intrusion Detection Systems, DDoS Attacks, Economic Security, Financial Cybersecurity, Adaptive Defense, Digital Services Resilience

References

  1. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human -level control through deep reinforcement learning. Nature, Vol. 518, No. 7540. pp 529 -533.
  2. Van Hasselt, H., Guez, A., & Silver, D. (2016). Deep reinforcement learning with double Q -learning. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30, No. 1. pp. 2094 -2100.
  3. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). Cambridge, MA: MIT Press. -552 pp.
  4. Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2016). Continuous control with deep reinforcement learning. International Conference on Learning Representations (ICLR). -Conference paper.
  5. Nguyen, T. T., & Reddi, V. J. (2021). Deep reinforcement learning for cyber security. IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 8, pp. 3378 -3391.
  6. Islam, R., Khatun, M., & Hossain, M. A. (2022). DDoS attack detection using machine learning techniques in cloud computing environments. Journal of Network and Computer Applications, Vol. 191. -Article 103160.
  7. Satpathy, S., Panda, M., & Sahoo, K. S. (2025). Deep reinforcement learning - based intrusion detection for adaptive DDoS mitigation in cloud networks. Future Generation Computer Systems, Vol. 149. pp 78 -91.
  8. Bouhardouz, Y., Chikhi, S., & Lagraa, N. (2025). Reinforcement learning - based DDoS mitigation in software -defined networks with quality -of-service awareness. Computer Networks, Vol. 238. -Article 110022.
  9. Asmar, Y., & Tuqan, J. (2024). Cost -aware intrusion detection systems for financial infrastructures. Computers & Security, Vol. 133. -Article 103286.
  10. FS-ISAC. (2025). Operational resilience and DDoS threat management in financial services. New York: Financial Services Information Sharing and Analysis Center. -Industry report.
  11. ENISA. (2023). Threat Landscape for Distributed Denial -of-Service Attacks. Athens: European Union Agency for Cybersecurity. -64 pp.

Downloads

Download data is not yet available.