Intelligent intrusion detection: leveraging reinforcement learning for economic security against

DDOS THREATS, Bekov Sanjar Nigmandjanovich

Marketing · 2026-yil

Annotatsiya

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.

Maqola ma’lumotlari
MualliflarDDOS THREATS, Bekov Sanjar Nigmandjanovich
JurnalMarketing
NashriyotТошкент давлат иқтисодиёт университети
ISSN3060-4621
Nashr sanasi2026-01-31
Son1
TilIngliz
Ilmiy sohaIqtisodiyot
DOI10.67668/mj/2026iss1/702
Indeksga qo‘shilgan2026-10-18

DOI: 10.67668/mj/2026iss1/702 · Maqolaning asl sahifasi

Kalit so‘zlar

reinforcement Learning, Intrusion Detection Systems, DDoS Attacks, Economic Security, Financial Cybersecurity, Adaptive Defense, Digital Services Resilience, обучение с подкреплением, системы обнаружения вторжений, DDoS -атаки, экономическая безопасность, кибербезопасность финансовых систем, адаптивная защита, устойчивость цифровых сервисов, mustahkamlovchi oʻrganish, hujumlarni aniqlash tizimlari, DDoS hujumlar, iqtisodiy xavfsizlik, moliyaviy kiberxavfsizlik, moslashuvchan himoya, raqamli xizmatlar barqarorligi

Ilmiy soha

Iqtisodiyot

Iqtibos keltirish

APADDOS THREATS, & Bekov Sanjar Nigmandjanovich (2026). INTELLIGENT INTRUSION DETECTION: LEVERAGING REINFORCEMENT LEARNING FOR ECONOMIC SECURITY AGAINST. Marketing, (1). https://doi.org/10.67668/mj/2026iss1/702
GOST (OAK)DDOS THREATS, Bekov Sanjar Nigmandjanovich INTELLIGENT INTRUSION DETECTION: LEVERAGING REINFORCEMENT LEARNING FOR ECONOMIC SECURITY AGAINST // Marketing. – 2026. – № 1. – DOI: 10.67668/mj/2026iss1/702.
MLADDOS THREATS, and Bekov Sanjar Nigmandjanovich. “INTELLIGENT INTRUSION DETECTION: LEVERAGING REINFORCEMENT LEARNING FOR ECONOMIC SECURITY AGAINST.” Marketing, (1), 2026. https://doi.org/10.67668/mj/2026iss1/702
ChicagoDDOS THREATS, and Bekov Sanjar Nigmandjanovich. “INTELLIGENT INTRUSION DETECTION: LEVERAGING REINFORCEMENT LEARNING FOR ECONOMIC SECURITY AGAINST.” Marketing, (1) (2026). https://doi.org/10.67668/mj/2026iss1/702
HarvardDDOS THREATS, and Bekov Sanjar Nigmandjanovich (2026) ‘INTELLIGENT INTRUSION DETECTION: LEVERAGING REINFORCEMENT LEARNING FOR ECONOMIC SECURITY AGAINST’, Marketing, (1). https://doi.org/10.67668/mj/2026iss1/702
BibTeX
@article{ddosthreats2026,
  title = {INTELLIGENT INTRUSION DETECTION: LEVERAGING REINFORCEMENT LEARNING FOR ECONOMIC SECURITY AGAINST},
  author = {DDOS THREATS and Bekov Sanjar Nigmandjanovich},
  journal = {Marketing},
  year = {2026},
  number = {1},
  doi = {10.67668/mj/2026iss1/702},
  url = {https://doi.org/10.67668/mj/2026iss1/702}
}

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