"Digital economy" scientific and electronic journal
Published July 1, 2025 | Version v1
Journal article Open

AXBOROT XAVFSIZLIGIDA SUN'IY INTELLEKTNING ROLI: TAHDIDLARNI ANIQLASH VA OLDINI OLISH

Authors/Creators

  • 1. Qarshi davlat texnika universiteti assistent o'qituvchisi
  • 2. Qarshi davlat texnika universiteti stajyor o'qituvchisi

Description

Mazkur maqolada zamonaviy axborot xavfsizligi tahdidlariga 
qarshi kurashishda sun’iy intellekt (SI) texnologiyalarining tutgan o‘rni chuqur tahlil 
qilinadi. Global miqyosda raqamli transformatsiyaning jadallashuvi bilan birga 
kiberxavfsizlik muammolari ham ortib bormoqda. Shunday bir vaziyatda, an’anaviy 
xavfsizlik choralarining o‘zi yetarli bo‘lmay qolmoqda. Maqolada sun’iy intellekt 
algoritmlarining kiberhujumlarni oldindan aniqlash, hujumlarni tasniflash, real vaqtli 
monitoring va avtonom qaror qabul qilish kabi imkoniyatlari yoritilgan. Bundan 
tashqari, mavjud ilmiy manbalar asosida tahdidlarni aniqlashning zamonaviy uslublari 
ko‘rib chiqilgan va O‘zbekiston sharoitida SI asosida xavfsizlikni mustahkamlash 
bo‘yicha tavsiyalar ishlab chiqilgan. 

Abstract (Russian)

В данной статье подробно анализируется роль технологий 
искусственного интеллекта (ИИ) в противодействии современным угрозам 
информационной безопасности. С ускорением цифровой трансформации во всем 
мире увеличиваются и проблемы кибербезопасности. В таких условиях 
традиционных мер безопасности становится недостаточно. В статье освещаются 
возможности алгоритмов ИИ в раннем выявлении кибератак, классификации 
угроз, мониторинге в реальном времени и принятии автономных решений. 
Кроме того, на основе существующих научных источников рассмотрены 
современные методы выявления угроз, а также разработаны рекомендации по 
укреплению безопасности на основе ИИ в условиях Узбекистана.

Abstract (English)

This article provides an in-depth analysis of the role of artificial 
intelligence (AI) technologies in countering modern information security threats. As 
digital transformation accelerates globally, cybersecurity issues are also increasing. In 
such a context, traditional security measures are no longer sufficient. The article 
highlights the capabilities of AI algorithms in early detection of cyberattacks, threat 
classification, real-time monitoring, and autonomous decision-making. Furthermore, 
based on existing scientific sources, modern methods for threat identification are 
reviewed, and recommendations are proposed for strengthening security using AI in 
the context of Uzbekistan. 

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11. Sharopova B.A., Jabborov E.E., Sirojev N.G‘. - AXBOROT XAVFSIZLIGIDA SUN’IY INTELLEKTNING ROLI TAHDIDLARNI ANIQLASH VA OLDINI OLISH.pdf

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Additional details

Additional titles

Alternative title (Russian)
РОЛЬ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ОБЕСПЕЧЕНИИ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ: ОБНАРУЖЕНИЕ И ПРЕДОТВРАЩЕНИЕ УГРОЗ
Alternative title (English)
THE ROLE OF ARTIFICIAL INTELLIGENCE IN INFORMATION SECURITY: THREAT DETECTION AND PREVENTION

Software

Repository URL
https://infocom.uz/magazine/18
Development Status
Active

References

  • 1.https://uza.uz/oz/posts/akt-sohasidagi-islohotlar-natija-va-maqsadlar_265851 2. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, https://doi.org/10.1109/COMST.2015.2494502 18(2), 1153–1176. 3. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE Symposium on Security and Privacy (pp. 305–316). IEEE. https://doi.org/10.1109/SP.2010.25 4. Javaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016). A deep learning approach for network intrusion detection system. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) (pp. 21–26). https://doi.org/10.4108/eai.3-12-2015.2262516 5. Li, Y., Xu, Y., & Zhang, Q. (2020). Machine learning for cybersecurity: A survey. Journal of Computer Science and Technology, 35(1), 8–20. DOI:10.1007/s11390-019-1917-8. 6. Moustafa, N., & Slay, J. (2016). The evaluation of network anomaly detection systems: Statistical analysis of the UNSW-NB15 dataset and the comparison with the KDD99 dataset. Information Security Journal: A Global Perspective, 25(1–3), 18–31. https://doi.org/10.1080/19393555.2015.1125974 7. https://inlibrary.uz/index.php/science-research/article/view/102619 8. Xhafa, F., & Barolli, L. (Eds.). (2020). Advances in intelligent networking and collaborative systems. Springer. https://doi.org/10.1007/978-3-030-43601-0 9. O'zbekiston Respublikasi Raqamli texnologiyalar vazirligi. (2023). Kiberxavfsizlik bo'yicha milliy strategiya loyihasi. https://www.mitc.uz 10. European Union Agency for Cybersecurity (ENISA). (2022). Artificial intelligence cybersecurity challenges. https://www.enisa.europa.eu